{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1 align=\"center\"> Logistic Regression </h1>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# initally do with normal preloaded sklearn dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The MNIST database of handwritten digits is available on the following website: [MNIST Dataset](http://yann.lecun.com/exdb/mnist/)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Four Files are available on this site:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[train-images-idx3-ubyte.gz:  training set images (9912422 bytes)](http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz) \n",
    "<br>\n",
    "[train-labels-idx1-ubyte.gz:  training set labels (28881 bytes)](http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz)\n",
    "<br>\n",
    "[t10k-images-idx3-ubyte.gz:   test set images (1648877 bytes)](http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz) \n",
    "<br>\n",
    "[t10k-labels-idx1-ubyte.gz:   test set labels (4542 bytes)](http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np \n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.linear_model import LogisticRegression \n",
    "\n",
    "# Used for Confusion Matrix\n",
    "from sklearn import metrics\n",
    "import seaborn as sns\n",
    "import pandas as pd\n",
    "\n",
    "# Used for Loading MNIST\n",
    "from struct import unpack\n",
    "import numpy as np\n",
    "import matplotlib.pylab as plt \n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can download the data via command line (you can see this on the youtube video) or you can get them from the website or my github. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Downloading MNIST Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--2017-06-24 15:19:48--  http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz\n",
      "Resolving yann.lecun.com (yann.lecun.com)... 216.165.22.6\n",
      "Connecting to yann.lecun.com (yann.lecun.com)|216.165.22.6|:80... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 9912422 (9.5M) [application/x-gzip]\n",
      "Saving to: ‘data/train-images-idx3-ubyte.gz’\n",
      "\n",
      "data/train-images-i 100%[=====================>]   9.45M  2.19MB/s   in 6.1s   \n",
      "\n",
      "2017-06-24 15:19:55 (1.54 MB/s) - ‘data/train-images-idx3-ubyte.gz’ saved [9912422/9912422]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "!wget -O data/train-images-idx3-ubyte.gz http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--2017-06-18 01:55:28--  http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz\n",
      "Resolving yann.lecun.com (yann.lecun.com)... 216.165.22.6\n",
      "Connecting to yann.lecun.com (yann.lecun.com)|216.165.22.6|:80... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 28881 (28K) [application/x-gzip]\n",
      "Saving to: ‘data/train-labels-idx1-ubyte.gz’\n",
      "\n",
      "data/train-labels-i 100%[=====================>]  28.20K  --.-KB/s   in 0.09s  \n",
      "\n",
      "2017-06-18 01:55:28 (307 KB/s) - ‘data/train-labels-idx1-ubyte.gz’ saved [28881/28881]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "!wget -O data/train-labels-idx1-ubyte.gz http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--2017-06-18 01:55:29--  http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz\n",
      "Resolving yann.lecun.com (yann.lecun.com)... 216.165.22.6\n",
      "Connecting to yann.lecun.com (yann.lecun.com)|216.165.22.6|:80... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 1648877 (1.6M) [application/x-gzip]\n",
      "Saving to: ‘data/t10k-images-idx3-ubyte.gz’\n",
      "\n",
      "data/t10k-images-id 100%[=====================>]   1.57M   953KB/s   in 1.7s   \n",
      "\n",
      "2017-06-18 01:55:30 (953 KB/s) - ‘data/t10k-images-idx3-ubyte.gz’ saved [1648877/1648877]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "!wget -O data/t10k-images-idx3-ubyte.gz http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--2017-06-18 01:55:31--  http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz\n",
      "Resolving yann.lecun.com (yann.lecun.com)... 216.165.22.6\n",
      "Connecting to yann.lecun.com (yann.lecun.com)|216.165.22.6|:80... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 4542 (4.4K) [application/x-gzip]\n",
      "Saving to: ‘data/t10k-labels-idx1-ubyte.gz’\n",
      "\n",
      "data/t10k-labels-id 100%[=====================>]   4.44K  --.-KB/s   in 0s     \n",
      "\n",
      "2017-06-18 01:55:31 (15.4 MB/s) - ‘data/t10k-labels-idx1-ubyte.gz’ saved [4542/4542]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "!wget -O data/t10k-labels-idx1-ubyte.gz http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "gzip: can't stat: data/*.gz (data/*.gz.gz): No such file or directory\r\n"
     ]
    }
   ],
   "source": [
    "# decompress gzipped file\n",
    "# !info gzip\n",
    "!gzip -d data/*.gz"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loading MNIST Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def loadmnist(imagefile, labelfile):\n",
    "\n",
    "    # Open the images with gzip in read binary mode\n",
    "    images = open(imagefile, 'rb')\n",
    "    labels = open(labelfile, 'rb')\n",
    "\n",
    "    # Get metadata for images\n",
    "    images.read(4)  # skip the magic_number\n",
    "    number_of_images = images.read(4)\n",
    "    number_of_images = unpack('>I', number_of_images)[0]\n",
    "    rows = images.read(4)\n",
    "    rows = unpack('>I', rows)[0]\n",
    "    cols = images.read(4)\n",
    "    cols = unpack('>I', cols)[0]\n",
    "\n",
    "    # Get metadata for labels\n",
    "    labels.read(4)\n",
    "    N = labels.read(4)\n",
    "    N = unpack('>I', N)[0]\n",
    "\n",
    "    # Get data\n",
    "    x = np.zeros((N, rows*cols), dtype=np.uint8)  # Initialize numpy array\n",
    "    y = np.zeros(N, dtype=np.uint8)  # Initialize numpy array\n",
    "    for i in range(N):\n",
    "        for j in range(rows*cols):\n",
    "            tmp_pixel = images.read(1)  # Just a single byte\n",
    "            tmp_pixel = unpack('>B', tmp_pixel)[0]\n",
    "            x[i][j] = tmp_pixel\n",
    "        tmp_label = labels.read(1)\n",
    "        y[i] = unpack('>B', tmp_label)[0]\n",
    "\n",
    "    images.close()\n",
    "    labels.close()\n",
    "    return (x, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train_img, train_lbl = loadmnist('data/train-images-idx3-ubyte'\n",
    "                                 , 'data/train-labels-idx1-ubyte')\n",
    "test_img, test_lbl = loadmnist('data/t10k-images-idx3-ubyte'\n",
    "                               , 'data/t10k-labels-idx1-ubyte')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(60000, 784)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_img.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Showing Training Digits and Labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1187b7dd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,4))\n",
    "for index, (image, label) in enumerate(zip(train_img[0:5], train_lbl[0:5])):\n",
    "    plt.subplot(1, 5, index + 1)\n",
    "    plt.imshow(np.reshape(image, (28,28)), cmap=plt.cm.pink)\n",
    "    plt.title('Training: %i\\n' % label, fontsize = 20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
      "   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
      "   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
      "   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
      "   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
      "   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
      "   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
      "   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
      "   0   0   0   0   0   0   0   0   3  18  18  18 126 136 175  26 166 255\n",
      " 247 127   0   0   0   0   0   0   0   0   0   0   0   0  30  36  94 154\n",
      " 170 253 253 253 253 253 225 172 253 242 195  64   0   0   0   0   0   0\n",
      "   0   0   0   0   0  49 238 253 253 253 253 253 253 253 253 251  93  82\n",
      "  82  56  39   0   0   0   0   0   0   0   0   0   0   0   0  18 219 253\n",
      " 253 253 253 253 198 182 247 241   0   0   0   0   0   0   0   0   0   0\n",
      "   0   0   0   0   0   0   0   0  80 156 107 253 253 205  11   0  43 154\n",
      "   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
      "   0  14   1 154 253  90   0   0   0   0   0   0   0   0   0   0   0   0\n",
      "   0   0   0   0   0   0   0   0   0   0   0   0   0 139 253 190   2   0\n",
      "   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
      "   0   0   0   0   0  11 190 253  70   0   0   0   0   0   0   0   0   0\n",
      "   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0  35 241\n",
      " 225 160 108   1   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
      "   0   0   0   0   0   0   0   0   0  81 240 253 253 119  25   0   0   0\n",
      "   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
      "   0   0  45 186 253 253 150  27   0   0   0   0   0   0   0   0   0   0\n",
      "   0   0   0   0   0   0   0   0   0   0   0   0   0  16  93 252 253 187\n",
      "   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
      "   0   0   0   0   0   0   0 249 253 249  64   0   0   0   0   0   0   0\n",
      "   0   0   0   0   0   0   0   0   0   0   0   0   0   0  46 130 183 253\n",
      " 253 207   2   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
      "   0   0   0   0  39 148 229 253 253 253 250 182   0   0   0   0   0   0\n",
      "   0   0   0   0   0   0   0   0   0   0   0   0  24 114 221 253 253 253\n",
      " 253 201  78   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
      "   0   0  23  66 213 253 253 253 253 198  81   2   0   0   0   0   0   0\n",
      "   0   0   0   0   0   0   0   0   0   0  18 171 219 253 253 253 253 195\n",
      "  80   9   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
      "  55 172 226 253 253 253 253 244 133  11   0   0   0   0   0   0   0   0\n",
      "   0   0   0   0   0   0   0   0   0   0 136 253 253 253 212 135 132  16\n",
      "   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
      "   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
      "   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
      "   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
      "   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
      "   0   0   0   0   0   0   0   0   0   0]\n"
     ]
    }
   ],
   "source": [
    "# This is how the computer sees the number 5\n",
    "print(train_img[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Checking Performance Based on Training Set Size"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A confusion matrix is a table that is often used to describe the performance of a classification model (or \"classifier\") on a set of test data for which the true values are known. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "regr = LogisticRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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mwey8+I59oeKtoyLihHb2ERHHAheWFs9AdZaHj9J/NO91wE4R8d+K9auO94diP2VfbLFZ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gBAlTsi2b1ze5bp5SveTsKFC1xK\nScZutzvfQVm8ZGleGjU+y7ynTh6n2p01Aah2Z012bPW+mx/q17p8/8y829rap09mbuPNe2hYu1K2\ny3R6cQ5bdh0BIMDf3yvbwPqRZVi+3toP1+84Qh0j82GtVcre7NJPSWfNlkM0rFGGyIq3ERpUgMVv\n/5tvJj5JvWpWG9Lxlf+westBCgT4cWuRcOIuJOdLTDnxtZwd3JvXdXxhDqs373XkOBHO3Meb1I90\nGUPacfjKHCdL3/sADWuUA2BM72hmLvyVv05lPiWg5C0F+WXbIQDWbj1I/TvLei6QXHJnu9TppY/Z\n8qdr3p6KJ+R1APm446cNKGcYhi2nma/FMIxwoAKZ70C+7mehGIZRzTCMBYZhzDEMoxnwB7DDMIw2\neSnb9YoICyYuIbMjkpaejr+/9d9dMCyY8y4HcHxiMgXDgwFYsGILl1LTsqyrbIkinI1PpHXvGRw6\nfo6BTzX1QATXJyI0KEvHKy3N7hJvEOddpsUnpjjjTUtLZ+bLjzJhwAN8tswaRPz1j0MMm/YNUc/O\nZN/RM7zU2Tuvgo4IC7psG18Wc8JlMYcFk5h8iUmfreGBgR/R9+2vmTP8Yfz9/bDZbFnmLRQW7LlA\ncikp8QKhYeHO3/38/UhLy1pR/bpmJaXLVaBkmXJZvp//yWwe69TN+futxUuy/XfrERS/rl3JxSTv\na9AiwoKzNLRpaTkcwwnJFAwPyXaZX7cfYNikhUR1ncK+I6d5qXsLzwWSSxHhwVmPYdf9OfzyOuti\nZp314+9X1FlZ9+dkCoWH/J1Fv2HW9squnr6s3kqw6umEpBQuJCYTHhrE3Dc7MXKGdfe8S8iOmL3v\nGL6RbZzdMvuPnHYm6q3uq05YiPdd6BMRFnSN7XtlO5xl+4550rl9yxZ3tMN937XaYQ9d5Xc9/hfb\npP/FnO7XHQcZNmUxUT1irPagW3O80RX1Y455XTIFw1zyuuGPMWFAWz77zsrr0tPtlLntJjbNfZ6i\nhcLY+udfHowkd9x5/Bz46xwb/jji8RiuR2JiQtaczs//ipxuw5qVlClX0ZnTBYeEcuLYX/R56mFi\nx79Om4cfB+D2KnfwVI/+vDH5PW4rUZLPPnjXY3FcD3fmOQeOnmHDtgMeKPWNuyInzbJPB1+W47jG\ne2W9dey0NRB1T2Q5erZvxNS5P//dxb9uvpfj+Nb2BV9sl4KJS8g8sX9FXzThsnjDg3miVS1Onkvg\n+/VZB/W8oZ/iLTkduLefX7Z4UauP0CuGQ8fOMvDpZp4LJJd8M17f6eMDJCYkEBZ+WV6XmpnXla1Q\niee6Pk6vTo9Qr34jwiMKAtCgcTPnBYEZbitRkq2brUfir1v98z//XF1iMgUjgrNd5pjjYpt7apSn\nZ4f7mPrJCg9FkXsRoYFZ24N0O/7+1s5p9VMyp8UnufRT5q3lgUGf0Hf8Eua83A5/f5vV/t1aiE0f\n9KJooVC27jl+xd/Lb76Ws4N78zorxynMps+HUPSmcLY6Bhu9iXWuznWfdjmGQ682hhTEEy1rcvJc\n4hU5zv6jZ2lYsxwArRoYhIUUwNu4s106dtoaPL/nzrL0fKwBUz9d6ZEY8jqAvNPlc0GgUR7X9ygQ\nCM4LorbnMG92ZgATgZ+AL4B6QC1gaA7LuF18wkUiXO4q8bPZnM+cP59wkfDQzMQjIjSIuPjsG+XT\ncQksWWn9VyxduZ3aLlcqeIv4xOSs8fq5xpuc5Q6biNDALAdOt1FfENlhArEvtiM0uACLft7BZtOq\n4Bb9vIMalTPvAvEm8QnJOWzjq8f856HTfOq4I2X34dOcOZ9E8aLhpKfbr5jX24SEhpOUlHlXSXq6\nHX//rMnmz98vJarNw1m+S7gQz5FDB7izVl3nd71feJX5c+cwYmBPCt1UhIKFbvp7C38D4hMuEuEy\naOLn53fZMeyyfcOsYzi7ZRb9uIXNf1hXRC36cQs1XO6E9BbxFy4SEZbN/nzh8jorOMc6K+v+HERc\nfOLfUOK8s7ZXLo/hsCDnUxJKFSvEt9N7Mvebjcz7zroi98qYve8YvpFtnN0y3UfOZfAzUSyd3puT\nZ+I5fc777ji7oo6+vF0Kc62jg5wJe6lihfg2tgdzv9nEPMeFTafjElmy0rpDb+mqHdSu6oXH8P9m\nm/Q/l9MtWrGVzTutt74s+mkrNYyS2c6bn66oH3PM64KydHi7vf4fItu/TeyLDxMabHXgDh47x53t\n32bWV+sY+1zmY0e9hTuPn3+C0NCwLHcK29PTr8jpflq+1PnkGIDF//mEWnXvJfajBUyc9RmTx7xC\nSkoydzdqSiWjGgD3NGzK3j934o3cmef8E1j1lktOenm95doGXnZS42oejarJlKGP0a7/TE55Y5vv\nczmOb21f8MV26SIRLk9iyxrvxSvjjU/iqdZ1+FfdSnw3tQuRtxfnveGPcWuRy/O6oPzK67wipwP3\n9vNPxyWw5GfrlWtLV26jdjUvPFfnk/H6Th8fIDQsa16Xbk/H3zEwvG/3LjasXcXsz5cw+/OlnDt7\nllUrlmW7rueHvsbnH89m2HPduamwF5+ry7aff5HwsCv7Zjkt82jz2kwZ1pF2/aZz6uzf/y7R6xWf\nmJK1PbDZnO8ttvbpzGkRIa79FOtY3X34jNVPcTzq+eDxOO78dwyzFm1kbG/vu5jZ13J2cH9ed/DY\nWe58+A1mfbmasS6PAPcWVt/78n3aEW/i1fveT7Wqzb/qVuS7Kc8QWek23nvpYW4tEk73N79i8BON\nWDrpaU6eTeB0nPedf3ZnuwTwaLMaTHnxEdo9/57H8vY8DSCbprkZOEbmHcOv3Oi6DMO4CRjhsq6T\npmneyLPf/EzT/Nk0zQ+ABaZpnjBN8zzgmXu6Hdb+vp/oBta7ZepVL8u2PZlXte7cd5xKpW+mcMFQ\nCgT406BWBdZtzf6KmLW/7SO6vrWuhrUr8MfeY39v4W/A2i0HiL7XekxtvTtKs21PZhl37j9BpdJF\nKRwRYsVbszzrth7k8RY1GfTk/QAkXrxEerqd9HQ7iyc+w12Ok/NN7qrI5p3eeRfH2m0Hib7HeoRN\nvWql2LY388qtnQdOUqlUkcyYa5Rl3fZDPNWqlvPZ/sWLRhARGsRfpy/w259/0chxxUzzu29n9Rbv\nu0KqSvUabFq3GoBdO7ZStkKlK+bZs+sPjDtqZPlux++biKxdN8t3G9f9l/7DRjFi/Aziz8cRWefu\nv6/gN2jtb3uJbmCdEK13Zzm27c68amvnvmNUKnNL5jFcuxLrtuzLdpnFMb246w7rETlN6lV2DiZ7\nk7W/78sse/WyWePdf3m8FVnneLTf1fxmHqZRHWv/aF6/Gqs37/1by36j1v6+n+j61rvY61Uvk7Xe\nctbTGfVWBdZt3U+xIuEsntqdl6ct4cPFmY/B+W3XURo57lZpXr8Kq3/zvphvZBtnt0zLhtV45uUP\nadUrhqKFwvhhnen5gK5h7Zb9zrazXvUybNudw/Z1tMPFioSzeEo3Xp62NMv2tf4frH2lYa0K/LHX\n+67U/R9tk/7ncrrFU3twl+ORaU3q3s7mPw7/vYW/QVZe56gfr5rXudaP5Vi37SCPt6jFoE6NAUde\nZ7eTbrfzn3GdqFiqKAAXEpOznIzzFu48fv4JqlSvyUZHTmfu2JJNTrfD+boRgLCIgs67liMiCpGa\nmkp6WjojB/dm1x/bAPh903oqVvbOd2u6M8/5J7Didam3dl9eb92Std7KId6OLevQs30jonvEsP/I\n6b+76DfE53IcH9u+4IPt0taDRN9rABnxurRL+09SqZTL+ZUa5Vi37RBRvWfRvM8sovu+x5Y//6LL\n6//h+JkL/LbrLxrVsh5x3fzeyqz+fX9+hOQVOR24t5+/9re9RDe03i/ZsHZF7zxX52vx+lgfH6xH\nTW9Y+18Adm7fQrkKmY/aDgsPJzAoiMCgYPz9/bmpcGEuxJ/PblVsWLOKwa+8wRuT3+V8XBy16t7z\nt5f/ernuh7nap3/fl+0yHVvVpWeH+4juNtlr28C1Ww8SfbeVq9erVpJt+044p+08cMrRTwmmQIAf\nDWqUYd32wzzVqiZjelvvcy5eNNzqp5yJ5z9vdKBiySKAo/2ze2H752M5O7g3r/vPhC5ULH0zkJHj\npP+tZb8RVo7jGEOqVopte1326exynL6zad53NtH95rBl9zG6jJ7P8TMXaFnf4JnXvqBV//cpWiiE\nHzZ43+vm3NkudWxRm57tGxDdazr7j57xWAzueG/xXGCA43MTwzBGmKY54npWYBhGQeArIOPFBXbg\n8xssj2kYxiygu2maTzvW/yLWQLfHLPxpK03vrsyK9/piw0b31z6jQ3RtwkIDmf3VLwyZtJDFU7tj\ns9n4cPF6jp6My3ZdL05aROzL7en+aH3iLlzk6Zc/9mAkubPw5x00rVuJFe/0wGaz0X30l3SIqmHF\nu3ADQ6YsZfGkZ6x4v97I0VPnWfjTdt596VGWx3ajQIA/gycv4WJKKv3eWsiEAQ9wKTWN42cu0HvM\nV/kd3lUtXLmTpndVZEVsF2xA9zEL6dDsTsJCApm9eCNDpn3H4refwOZn48Olmzl6Kp73l2xm5tCH\n+GFaZ+x2Oz3HLiQtLZ0XY5YR+8IDBAb4s/PAKeb/dOW76PLb3Q2bsGXjOob1eQY7dnq/8CqrfviG\npKQkmrd5mLhzZwkNDcvy6FOAI4cOcGvxrHfrFS9ZhhGDehEUFMwdte6izj0NPRlKrixcsYWm9xis\nmPM8Nht0H/EJHVrUISw0iNnz1zBkwgIWx/TC5ufHhwt/4ejJuKsuA9Dvzc+Z8MKj1j59+jy9R83L\n5+iutHDFFprebbBidn+r7CPnWvGGBDL7q7UMmfAVi6f1svZnR7zZeXHiAmJf7khgAX927jvO/B+8\n7x3XAAt/2mbV07P6WDG/No8O0bWsmBesY8ikxSye4lpPn+ftAQ9yU8EQhnaOYmhnKyF/sP9MXpy8\niNhhjzliPsH8H73v3Zc3so2vtgzA7oMnWTq9N0kXL/Hzr3/y3Wrvq7MW/rSNpvVuZ8XM3la79Po8\nOjSvaR3DGdt3cjcr3sUbHNu3LTcVDGVo52YM7Ww9ku3B52fx4uTFxA57jO4P32u1w6/MzeforvQ/\n2ib9z+V0/cZ8wYTBDzvag3h6v3Gj6e7fa+HP22larxIr3u1l7U+jv6BD8xqEhQQxe+F6hkxZwuKJ\nna396etfOXryPAt/2sa7Lz/G8tgeFAjwY/Ckr7mYnMr4j35i5vDHSLmURuLFSzz75pf5Hd4V3Hn8\n/BPc06gJv2/8hSF9nga7nb5DRvDz999wMSmR6AceIe7cWUIuy+naPvZvpo4dydB+nUm9lMoTXfsQ\nHBJCz+eHMnPKOPwDAihcpCjPDnw5/wLLgTvznH+ChSu2WvG+189qA0d+6qi3gqx4Jy5k8dQeVryL\n1mUbr5+fjfGD2nHo2Dk+e+sZAFZt3MOod7/1ZDjX5HM5jo9tX/DBdinj/MqM7i7nVyKteBdtYMjU\nb1g88Wkr91hinV/JzovTlhI7pJ3VT9l/kvkrtnkwEievyOnAvf38Fyd+Rezwx+n+aEPiLiTx9LAP\nPB3ONflcvD7Wxwe4976mbP71Fwb26gR26D90JD8tX0pSUiIt2z5Ky7aP8kLvpwkIKEDxkqVo1vLB\nbNdVonQZhvXvTlBwMJG16lL33rw+aNT9Fv74O03vqcKK9wdY9eOrH9OhxV2OfXo1Q8bPZ3GsdQ7A\nuU9fZRk/PxvjX3iUQ8fO8tl465V7qzb+yagZS/M5wqwWrtpJ07sqsCLGOqdu9VOqO/opmxgSs5zF\nb//binfpby79lAf5YerT2IGeYxeRlmZn/CermTn0QVJSHe3fuMX5Hd4VfC1nB/fldQDj3/+BmSP+\nj5RLqdY2ft0Lzz+v/MPR9+5qxfvmV5f1vb9l8fhOVrxLNnHU5Z3Hl9t96DRLJz1t5e2b9/HdL396\nMJLccVe71O759xg/8CEOHT/LZ2OfBmDVpj2Mmpn9UyXcxWbP49UmhmEUAfZgPcLahjX4+zEw1DSt\n5xAbhpFO5p3FUaZp/uj43g9oD4wGymWUCbgIVDRN87pfRuNY5wOmaS50+e4JYL5pmrm5j90eUnfA\ntef6H5G0YQIh9YfldzE8JmnNG4TcNyK/i+ExSStHALDtyD/jjpi8ql7SujMmpHa/fC6J5yRtmkJI\nnefyuxgek7RxMgAh9Qblc0k8I2n92wA+t41D7h6c38XwmKR1b/liu2S7xmyAW3I6QuoO8L7Lqv8m\nSRsmABBy74v5XBLPSFo7BsDnjp8/jnrn42X/DlVLhPlc+wcQctfz+VwSz0j6dSLggzmOj2xfsLax\nr7RJ4NIuNXgpn0viGUmrR4MHczrA7nP9fF+L10f6+JDZz9994p//+N3cqFQsBICQWn3yuSSek7R5\nGiH3v5bfxfCYpJ9f8bmcDnwwb290ww8f/sdJWvWaL7ZLV83r8nwHsmmaZwzDeBZr0Nju+ENPAP9n\nGMZmYJ9j1ozB5c6GYTwEVATuBQpdVjg71uDzdQ8eO8qTDiy87Dvvu2VXRERERLKlnE5ERETkn085\nnYiIiMg/kzseYY1pmp8ahlEWeIPMO439gbuAOi6z2oDHL/sdl2VswLumaU52R7lERERERERERERE\nRERERCT3/Ny1ItM0xwCPAufJvNs4Y2DY7vLPRtaB44zv0oCBpmn2cleZREREREREREREREREREQk\n99w2gAxgmuZ8wMC6E/ksmYPFrv8yZPx+CZgD3Gma5kR3lkdERERERERERERERERERHLPLY+wdmWa\n5gngZcMwRgC1gYZYg8qFgZuAJOAMcBhYDaw2TTPe3eUQEREREREREREREREREZHr4/YB5AymaaYC\n6x3/RERERERERERERERERETEy7n1EdYiIiIiIiIiIiIiIiIiIvLPpQFkEREREREREREREREREREB\n/sZHWBuGEQ7Ud/wrAxQFAoGzjn9/AGuA30zTTP+7yiEiIiIiIiIiIiIiIiIiIrnj9gFkwzDqAP2A\nx4CgXCxyyjCMGUCsaZrH3V0eERERERERERERERERERHJHbc9wtowjFDDMKYC64AngGDA5vh3NRnT\nbgFeBnYahtHeXeUREREREREREREREREREZHr45YBZMfjqpcBzzrWaQPsjn+QOVjs+g+XeWxAIeBT\nwzBed0eZRERERERERERERERERETk+rjrEdafYb3rGDIHhG3AFmAtsAM4ByQC4VjvQ44EGgHlyTrQ\nPMwwjOOmaU5zU9lERERERERERERERERERCQX8jyAbBjGg0Arsg4CLwcGmaa5NRfL/wuYANxJ5uDz\nW4ZhfGua5u68lk9ERERERERERERERERERHLHHY+wHur4mfFY6jdM04zOzeAxgGmaPwB1sO5iznj0\ndSDwhhvKJiIiIiIiIiIiIiIiIiIiuZSnAWTDMEoB9ch8l/FC0zRfvt71mKaZCjwJrHd8ZQMeMAwj\nIi/lExERERERERERERERERGR3MvrHcj3OH5m3H087EZXZJpmGjDQZV2BQOMbLpmIiIiIiIiIiIiI\niIiIiFyXvA4gl3D5fNA0zZ15WZlpmquB4y5flc7L+kREREREREREREREREREJPfyOoBcwPHTDpzI\n47oyHHL5HO6mdYqIiIiIiIiIiIiIiIiIyDXkdQD5iOOnDbgtj+vKUMTl819uWqeIiIiIiIiIiIiI\niIiIiFxDXgeQV2PdfQxQyjCMO/KyMsMwSgBlXb76PS/rExERERERERERERERERGR3MvTALJpmoeA\nlS5fvZm34jAQ8McalN5qmuaWPK5PRERERERERERERERERERyKa93IAM8B1xyfG5tGMaoG1mJYRiP\nAv2xBo/twAtuKJuIiIiIiIiIiIiIiIiIiORSngeQHXcJ9yDzUdZDDcP4wjCMUrlZ3jCMIMMwRgKf\nYr1L2QaMMk1zWV7LJiIiIiIiIiIiIiIiIiIiuReQ00TDMF65jnX9BtR2fG4HtDUMYxnwM7AVOAMk\nAsFAYaA8cA/QGrgZa+DYDswGFhmGUds0zU3X8fdFRERERERERERERERERCQPbHa7PduJhmGkk3ln\nca7W55jf5vg9N8tmN6/dNM0cB7j/JtcTr4iIiIhcH9u1Z3Eb5XUiIiIifw/ldCIiIiL/G66a1+V2\ngDY3SWHGu4sha2KX07Kuy1zv3xMRERERERERERERERERETfKzQBybgdzb2TQ1ysHikNq9cnvInhM\n0uZphNQdkN/F8JikDRMIqfNcfhfDY5I2TgYgpHa/fC6JZyRtmgLAp5uP5HNJPOfxWiV9rs4CCIka\nm88l8Yyk5UMACHn4vXwuieckze/iM3UWWPVWocc/yu9ieEzcp0969O/5ZP3oI8dPRpvva9vY1+Ld\nd+pifhfDY8rfHAz4zj7trLMaDs/nknhO0n9fJ+Su5/O7GB6T9OtEn4sXIKT1lHwuiWckLfF8vuEr\nOQ5YeY6vtAfgmzkO+M4+nZG37zmRlM8l8ZyKxUJ8bp/2yTbfR8YYnOML9Qblc0k8J2n924S0m5Xf\nxfCYpK+6ZjvtWgPII91bFBERERERERERERERERER8VY5DiCbpqkBZBERERERERERERERERERH+GX\n3wUQERERERERERERERERERHvoAFkEREREREREREREREREREBNIAsIiIiIiIiIiIiIiIiIiIOXjmA\nbBhGgGEYnfO7HCIiIiIiIiIiIiIiIiIiviTA3Ss0DKMsYACFgECsQWrbVWa1OaYFAEFAGFAYqALc\nDxQEZru7fCIiIiIiIiIiIiIiIiIicnVuG0A2DKMlMBqo4YbV2QC7G9YjIiIiIiIiIiIiIiIiIiK5\n5JYBZMMw+gETHb9e7W7j66GBYxERERERERERERERERGRfJDnAWTDMCKBCWQOHGcMAF/+u+t3XGWa\n6zx24Ie8lk1ERERERERERERERERERHLPHXcgD8V6l7HrwPFZYAsQB9wJlHdM3wrsxXrncVGs9x1H\nOKZlDBy/DkwxTfO0G8omIiIiIiIiIiIiIiIiIiK5lKcBZMMwQoGHyDoAPAhrADjVMU9nYJZjkW2m\naf7bZXk/oDUwFSjjWEcPICYv5RIRERERERERERERERERkevnl8fl62DdTQzW4PF7pmlOyBg8dvjR\n8dMGtHBd2DTNdNM0FwM1gF8cX98CvJPHcomIiIiIiIiIiIiIiIiIyHXK6wByRcfPjHcbT7h8BtM0\n9wOnHL/eZBhGjavMEwe0B0471tXWMIzGeSybiIiIiIiIiIiIiIiIiIhch7wOIN/i8vm0aZo7s5lv\nq8vnu642g2mah4FpLl89n8eyiYiIiIiIiIiIiIiIiIjIdcjrAHKg46cdOJzDfH+4fI7MYb73HT9t\nQDPDMAJzmFdERERERERERERERERERNworwPIF1w+X8xhvr0un6tmN5NpmgeAM45fg4H6N140ERER\nERERERERERERERG5HnkdQD7t8rlgDvPtc/y0AVWusc5DLp/L3UCZRERERERERERERERERETkBuR1\nAPmY46cNKG8YRnbrc70DuaRhGDkNNl9y+XxLtnOJiIiIiIiIiIiIiIiIiIhb5XUAeR2Q5vgcDDTP\nZr4/sd6TbHf8Xi+HdZbLY5lEREREREREREREREREROQG5LH0rDAAACAASURBVGkA2TTNeOA3l6/G\nGoYRfpX5EoE9WHcqAzx2tfUZhlEfuJnMgeYzV5tPRERERERERERERERERETcL693IAPMc/lcHVhv\nGEbUVeb70fHTBjxjGEYT14mOx1rHuMwDsNUN5RMRERERERERERERERERkVwIcMM63gEGkfm+4irA\nt4ZhHAAqmaaZ7vj+I6A71t3FAcA3hmF8AqwHbgW6AKUc023AaWCTG8onIiIiIiIiIiIiIiIiIiK5\nkOc7kB2Pse5K5mOnMwaAQ1wGjzFNczXwg2OaHQgEngZigVeB0o5ZM6a/Y5pmal7LJyIiIiIiIiIi\nIiIiIiIiueOOO5AxTfNrwzDaYw0GF8MaAN53lVm7AevIfM+xzWWa3eXzDmCUO8qWX2w2G5OHdSCy\nckmSU1Lp9don7D10yjm91X3VGda9Jalp6XywYC1zvlqT7TJVKtxGzMuPY7PB7oMn6fXaXNLS0nP4\n655ns9mYPOQRIm8vQfKlVHqN+py9h13ibVSNYV2bk5qazgeL1zNnwS/OaXXvKMOovm2I7hkLwC2F\nw4l5qT2FI0Lw9/ejy6tz2XfktMdjuhabzcbkFx8jsnIJa3u9/tllMd/BsG4tSE1L44NF65jz1dps\nl4msXJKpw9qTmpbOnwdO0Ov1z7Db7Tn8dc+z2WxMHvpY5v75+qdX7tPdoq19euEvmfHmsEyHFnXo\n1fE+Gj89MT9CylF6ejpLZk/m+IE9+AcUoG2PQRS9raRz+pE9O/nuw+nYsRNeqAgP9xmGn58fC2eM\n49zJ46SmpnBfuyeoclcDTh87woLpY7Fho1jpcrTq/Bx+fu54g4D7uLPOqlD6ZmaOfBK73c72PX/R\n/83PvW5/BrDZYHK/5kRWKEbypTR6TfiGvUfPOae3uqciw55oYMX87VbmfPM7TzSvzpPN7wQgODCA\nyIrFKNd+GnEJyQCM69mUXYfPMOvr3/IlppzYbDC5e30iyxW14o1dxd5j8VnmCQn0Z8mIlvSMWcWu\nI3EE+Nt4p/d9lC0WTlABf8Z88RtLNhx0zj/umbvZdSSOWct2ejqca3JnnfXhm09xa9GCAJQtUYT1\nW/fTaegH+RXaVdlsMKHz3VQvU5jk1DT6vfsLe49fuX0XDGtGn3fX8ufR887vby4YzM9vtOKhN77n\nz6PnqVGuCBO73E1yahpbD5xlyAcb8MJD2GPcWT9GVi7JhCGPkZZuJzklla7DP+TEmfgc/nr+cOfx\nc0vhcGKGP07hgiH4+/nR5ZWP2eeSL3kDd27jDOMGPsyuAyeY9cV/8yOkHPniPp2ens60t0ezd/cu\nCgQG8vyLr1KiVBnn9B+/W8KXn32In58/0W0eok279qSmXmLCG69y/K+jXLqUwuNPdefeRo3ZvesP\nXh3clxKlywLQ5qHHuL9Zi/wK7ap8La+z2WxMHtiGyEq3WTnOmAXsPXLGOb1VA4NhTze24l2yiTmL\nNzqn3XJTGGve60Xr599n10GXY7hvS3YdPMWshRs8GktuWH3KRzP73q/Pu7If2rW5Fe+idVf2vfs9\nQHQP6+1hVcrfSsxL7bHZbNa5hlHzvO5cA7g35gwdomvTq0MjGnee7LE4cstmg8nPNiGy/M3WPj3l\nB/b+Feec3qpeeYY9Xs+Kd/kO5ny3ncAAf959vhnlbyvE+cQU+k9fwZ6jcdxSKISYfv+icHgQ/n42\nuoxfzr5jcTn89f9t7sxxahilmD+5O7sPngRg5hf/5Ytlm/MrtKvytTbf1+IF39un09PTiZnwBvt2\n76JAgQI8NyRrTrdi2RLmf/YRfv5+NG/1EK0dOd3EN0dw4piV03Xs1I17GjZmz66djBjS17l8q4fa\nc/+/ovMrtKvyyX6Km9r7yMolmDD4EdLS061j+NVPOHHmgsdjuhafHF8Y8jCRtxcnOSWNXqM/Z+/h\nzHGfVg2rMaxrlCPeDcxZuI4Afz/eGd6BsiUKE1QggDGzv2fJqh1UKFWUma90xI6d7XuO0X/cV14Y\nL0zu0YDIckVIvpROr5hV7D12Pss81rnYVvSMWcmuI5k5Wt3bb2FUp3pED18CQM0KRZnaswHJl9LZ\nsu80A99b65FzdW4bwTBNcz5gAG8Au7nKALJpmvuBplgDxBmDx3YyB5NtWAPM/zJNM9ldZcsPbZtE\nEhwYQOOnxjN8ykLGDHjYOS0gwI9xAx+hTa9pRHWZRJdHGlCsSES2y7zW5wFembaIps9Yg2yt76ue\nLzHlpG3j6gQHBdC4yxSGT1vCmP5tndMC/P0Y9/xDtOnzDlE9YujS7h6KFQkHYMCTTYh9uQPBgQWc\n84/u14Z5324kqkcMI6Z/g1GumMfjyY22je+0Yn5mEsOnLmbM8w85p1nbuB1tescS1W0qXdrVt7Zx\nNsu81L0Fb8z8jn91mUxQYAAtG1bLr7Cy1bbJnQQHFqDx0xMdZW/nnOaM99lYorpOocvDjnhzWKaG\nUYqnHroHm812tT+X73b++l9SU1Lo+vo0mv1fN5Z9NN05zW63s+jd8TzY6wW6jJxCpZp1iTt1jC3/\nXU5IREE6j5zME0PHsnTOVAC++yiWpu0703nkZOyA+evqfIoqe+6ss8YOfIQRMV/TrMskbDYbDzS+\nM7/CylHbBpWt8j/3McPf+5kxPZo6pwX4+zGu579o8+I8ogbOpUvrGhS7KZSPl20jetCnRA/6lE1/\nHmNgzPfEJSRzc6EQFox+jNb3VsrHiHLWtl5Zggv403joYoZ/vIExT9+dZXrtijezfFRryt8a4fzu\n8fsrcebCRZq9vIS2r3/LxK73AtaA44KXm9O6bhm8lTvrrE5DPyC6+1Q6DJzFufgkXhj/VX6Fla02\nd5UmqIA/Ua9+y4hPNzPqiTpZpteqUIRvXo3Osn0BAvxtTOp6NxdT0pzfTe52Dy9++CstRy7jfOIl\nHmtQ3iMxeCt31o9vv/AoA8b+h+huk1n4428MfCYqv8LKkTuPn9HPPci8b34lqusURsQu8cq8zp3b\n+ObC4SyY1ovW93tn2we+uU+vWfkjKSkpTHr3Izr3fI53p47PMn1mzATGTH6XCTM+4MtPPyT+/Hl+\n/G4JBQvexPjp7zN6wnRiJ74JwJ87/+Dhjk/y1rT3eGvae143eAy+l9e1bVTVKnvPmQyfsYwxfTK3\nSYC/H+P6tqTNgA+I6jObLm3voljhMOe0aS+0JSnlknP+m28KZcHbT9K6YRWPx5FbbRtXt+LtPJnh\nU79mzPOX9b0HPEibPjOI6j6NLu3uzex7d2pK7PAOBAdm3kfwWu/WvBKzhKZdpgDQutEdng0ml9wZ\nM0ANoyRPPXg3XtoVpe29FQkO9KfxoP8w/P01jOnayDktwN+Pcd0a0Wb4AqJe/JIuLapT7KYQOre4\ngwsXL3H/wM8ZMOMnJvZsDMDozg2Yt8IkasiXjPjoF4zShfMpKu/gzhynVtXSTPl4BdHdpxLdfarX\nDbSB77X5vhYv+N4+vXbVCi4lJzNhxoc80/M5ZsVMyDJ9VsxE3pj0Dm/HfsD8eR8RH+/I6QoV4q2Y\nObz+dizTJ44B4E9zB+06PMnYqe8xdup7Xjd4DD7YT3Fje//2wHYMeOtLonvEsHDFFgY+9S+Px5Mb\nPje+cP8d1jbuMo3hMUsY89wDzmnWGFJb2vR9l6ge051jSI+3rMOZuASadY+l7XMzmTjYqrPG9m/L\niBnf0qx7rNVPud/78ti2d5ezzsW+uJjhH61nzDNXORc7ug3lb8t6rm7AQ5HE9m5EcAF/53fTejVk\n8Hu/0Oylr4lLTKHDfZ45B+3WW+BM04wzTfNl0zQNoFc282wH6gBPAYuA7cA24EvgcaC+aZon3FEe\nwzDy7QxV/VoVWb7mDwDWb91PnWqZJ9mrlL+NPYdOci4+iUupaazZvIeGtStlu0zHQbNYvWkPBQL8\nubVoQeIuXPR8QNdQv0Z5lq+x7kBbv+0AdaqWdk6rUv5W9hw+lRnvb/toWKsiAHsPn6bjC3OyrOve\nyPKULHYTS2J60rFFbVZu3OO5QK5D/ZoVMrfXtgPUqeYSc7nb2HPINea9NKxdMdtlfjMPU7hgKADh\noUFcSk3D29Svefn+6bqNs4v36ssUKRTKyD5tGPz2fM8HkksHd26jUs26AJS+vRpH95rOaaf/OkRo\neEF+WfoFc0b2J+lCPDeXKEO1exrTtH1naya7HT9/q5L/a+8uylWrAcDtNeuxd5v3vd7dnXVW7aql\nWbXxTwCWrd5Ok7u98wRc/TtKsXyDda3T+j+OUqfybc5pVcoUZc/Rs5y7kMyl1HTWbDtMw8jMfb52\n5duoVvZmZi/9HYCwkEBGf/Rf5n6/3bNBXIf6VW9j+eYjAKzfdZI6FW/OMj2ogB8dx/6Q5Wq3+Wv2\nMXKutb/asJHquCMlLDiA0fM2M/fn3R4q/fVzZ52VYXjPVkz/bCXHTmW9WtAb3GMU44ffjwLw6+5T\n1KpQNMv0wAB//j3+J3YdzXrHyah/12HO97v462yi87sSRUJZ/6d11fkv5gnuzb90Kov8yuvcWT92\nenEOW3ZZx2GAvz8Xky/hjdx5/Nxb05HXTe9Nx5Z3sfJX76s33LmNw0KCGD1jKXOXeN9dixl8cZ/e\nvmUzd91TH4Cq1SP5c2fW9rp8xdtJuBBPSkoy2O3YbNCoSXM6desNWBcP+jvyut3mDtavWcWgZ59h\nwpuvkpiQ4NlgcsHX8rr6kWVYvs6qW9ZvP0ydKplPDapS7hb2HDnDufiLVrxbDtKwZjkAxvRpwcwF\nG/jrVOYdZWEhgYyevYK533nf02Qy1K9ZgeVrc+h7u9bRv7v2vU/RcXDWvnfHF+awevNex7mGCOIu\nJHkukOvgzpiLFApl5LOtGTx+gecCuE71q5Vg+cYDAKw3j1GnUmYKVKV0Yfb8FZfZT9lxlIbVS1Kl\nTBGW/bofgD+PnKNK6SIA3Fu1BCVvDmfJ6Ifo2Nhg5ZbDHo/ncvl6rs6NOU6tqqVp0egOls/qx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gmWiWvN0a66141u46y+rtzulwukpel1X1o5ubiQlDOnH8zGW+n9ANgLVb9jPm02XOCi1DWXn+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\nvfdr2buffQwQ4FeaP7elfjfq9gP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vSzmtFCIiIiIiIiIiIiIiIiIi2YwrDiDbDxrnclop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iIiImJhSk5OtncZHI3+h4iIiIj8e0w2/FvK60RERET+HcrpRERERB4P98zr3Gxdiv8Cr1of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\nALD4BU3AVccp+4cTAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11f4c7dd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(nrows = 1, ncols = 3, figsize = (27,81));\n",
    "\n",
    "X_train = train_img[:50].reshape(50, 784)\n",
    "y_train = train_lbl[:50]\n",
    "regr.fit(X_train, y_train)\n",
    "fifty_score = regr.score(test_img, test_lbl)\n",
    "predicted = regr.predict(test_img)\n",
    "cm = metrics.confusion_matrix(test_lbl, predicted)\n",
    "cm_normalized = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n",
    "confusion_matrix = pd.DataFrame(data = cm_normalized)\n",
    "sns.heatmap(confusion_matrix, annot=True, fmt=\".3f\", linewidths=.5, square = True, cmap = 'Blues_r', ax = axes[0], cbar = False);\n",
    "axes[0].set_ylabel('Actual label', fontsize = 45);\n",
    "\n",
    "X_train = train_img[:100].reshape(100, 784)\n",
    "y_train = train_lbl[:100]\n",
    "regr.fit(X_train, y_train)\n",
    "hundred_score = regr.score(test_img, test_lbl)\n",
    "predicted = regr.predict(test_img)\n",
    "cm = metrics.confusion_matrix(test_lbl, predicted)\n",
    "cm_normalized = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n",
    "confusion_matrix = pd.DataFrame(data = cm_normalized)\n",
    "sns.heatmap(confusion_matrix, annot=True, fmt=\".3f\", linewidths=.5, square = True, cmap = 'Blues_r', ax = axes[1], cbar = False);\n",
    "axes[1].set_xlabel('Predicted label', fontsize = 45);\n",
    "\n",
    "X_train = train_img[:5000].reshape(5000, 784)\n",
    "y_train = train_lbl[:5000]\n",
    "regr.fit(X_train, y_train)\n",
    "five_thousand_score = regr.score(test_img, test_lbl)\n",
    "predicted = regr.predict(test_img)\n",
    "cm = metrics.confusion_matrix(test_lbl, predicted)\n",
    "cm_normalized = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n",
    "confusion_matrix = pd.DataFrame(data = cm_normalized)\n",
    "sns.heatmap(confusion_matrix, annot=True, fmt=\".3f\", linewidths=.5, square = True, cmap = 'Blues_r', ax = axes[2], cbar = False);\n",
    "\n",
    "plt.tight_layout()\n",
    "fifty = '50 Training Samples Score: {0}'.format(fifty_score) \n",
    "hundred = '100 Training Samples Score: {0}'.format(hundred_score) \n",
    "thousand = '5000 Training Samples Score: {0}'.format(five_thousand_score) \n",
    "axes[0].set_title(fifty, size = 30);\n",
    "axes[1].set_title(hundred, size = 30);\n",
    "axes[2].set_title(thousand, size = 30);\n",
    "fig.suptitle('Confusion Matrices', y=0.568, size = 55);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Using Logistic Regression on Entire Dataset "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Go over 4 step sklearn modeling pattern. \n",
    "# https://youtu.be/RlQuVL6-qe8?t=10m57s\n",
    "regr = LogisticRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Instantiate the estimator\n",
    "# Estimator is scikit-learns term for model \n",
    "# Instantiate means make an instance of\n",
    "# max_iter is known as a tuning parameter\n",
    "# all parameters not specified are set to their defaults\n",
    "regr = LogisticRegression(max_iter = 200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "          intercept_scaling=1, max_iter=200, multi_class='ovr', n_jobs=1,\n",
       "          penalty='l2', random_state=None, solver='liblinear', tol=0.0001,\n",
       "          verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fit the model with data, model training\n",
    "# learn relationship between features and response\n",
    "# train_img feature vector,\n",
    "regr.fit(train_img, train_lbl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Put in unknown image to model and asking to predict which digit (1,2,3,4,5,6,7,8,9)\n",
    "# predict for new images (can predict for multiple images at once)\n",
    "predicted = regr.predict(test_img)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.91810000000000003"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regr.score(test_img, test_lbl)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Confusion Matrixes with Different Number of Samples"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Done to show which class the model is consistently predicting incorrectly. This is called the confusion matrix."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "https://github.com/mGalarnyk/Python_Tutorials/blob/master/TensorFlow/Udacity/1_notmnist-Michael.ipynb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "cm = metrics.confusion_matrix(test_lbl, predicted)\n",
    "\n",
    "# also need to justify why you would do something like this. \n",
    "cm_normalized = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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uPwVzUiwN/IKZDPautTjNTZstmKvyv62sv2NOzsHA7VrrP617yA9jbn/swHwIzinM/fq8\nV/j/1BJMlWIr5mT2L53zPnjXnFmY2xQ7re1vxsxliLImGDqwbm+QM4O/trU839tCWuuXMJ9F0A4z\nKNqJeR0tIPe7J3pg5hDEY0rk2a+vVK3115i37z1ptZ+AOVk+5+4JWyf5tpgP21qP+dCsg5ir8uz9\nNAQzqHW3joseN5afrSxorc9hKgjHMe/WWIWp7rTS5tMo78SU++taWf5w+frUWscWzLst6mFeXwuB\nbzHvvhB+Liur4L+uJFtWUftkBSF8SClVEaiqtf7O5WfNMFWWClrrQ24bFwLrcwj26NwfiiSEKAB/\nnk0v8BPo9ZHFrsStUUBuGQhxKaHAN0qpAZirwLKYqskP/jYYEEJcWUXtclpuGQhxEVa5+FFMGfw3\nzFvHNDnzNYQQokiQWwZCCCGEB46dKfhbBjeUvHK3DKRCIIQQQgiZQyCEEEJ44kp/TkBB89cBQdHq\nZSGEEFfaFSu1FxX+OiAgJaOwE7gXEgQlGvYv7Bhu2TfHST4v2Debzx3y14z+ng+sfdwov7+n5B/s\nm2L9v//8PB/472swO19BK2pT8Px2QCCEEEL4syI2HpBJhUIIIYSQCoEQQgjhkaJ2y0AqBEIIIYSQ\nCoEQQgjhmaJVIpAKgRBCCCGkQiCEEEJ4QuYQCCGEEKLIkQqBEEII4YEiViCQCoEQQgghpEIghBBC\neETmEAghhBCiyJEKgRBCCOGBovbnj6VCIIQQQoirs0LgcDh4beyr7Naa4sWL88rocVSoWNG5PGH5\nMhbMe4/w8Ag6dupMl67dSE9PZ9TI4Rw9eoSAgABeGT2WSpWrcPDAAUa9OBybzUbVatUY+dIrBAR4\nN06y2WxMGxlNveo3k5qWQcyYD9h36IRz+X131GFkn3vJyHSwYNla5i1d47ZN5fLXMnv042RlZbFz\n7x88/8YnZHl548qX+epVv5nJw7qR6cgiNS2DXqMW8tepJK/y+TpjtgmDu7D7wF/M+ewnv8rn7/u4\nRuUbmPHSI9hssOfgcWLGLCYz0+F9vhHdcrY19sML8/WOMvni1zFv6Vq3bRa+8STXXxMJQMWbyrBh\n+36eGLHA+3z+3n9+nM/XGbP58hj2iaJVICj4CoFSyufbWPndf0lLTWPR4o95buBgJk0c71x2+vQp\nZk6P5b15i5i74H2+/CKBI0cO89OP35OZmcHCDz6ib8wzTJ82FYC3JrxB/2efZ/6ixWRlZbFq5Xde\n5+t4Vz1CigfR+slJjIqNZ/ygLs5lQUEBTBjclQ4xcbTrOZWeXVtQtkyE2zZvDu7KqzO+oG3Pqdhs\nNh5oXdev8r31wkMMevNTonpPI37lFgY/1c7rfL7OeG3pcJbFxXD/nd73XUHk8/d9PKb/A7wct5w2\nT00B4P476vggX11CihejdY8pjJqewPiBnfPk60yHfjNp1yuWnl2aW/nyb/PEiAVE9ZlO9OA5/J1k\n54VJS32Qz9/7z7/z+TpjQRzD4kIFUiFQSlUGJgNNgAxrULAdGKi13u3t+jdv2kjzlq0AqFe/ATt3\n7nAuO3zoMNWVomSpUgDUrlOXbVu3olQNMjIzcTgcJJ87R1Ax89R//XUnTW5tCkDLVnewdvVq7m7r\n3UmtecMqfLvmNwA2bN9P41oVnMtqVLqBvYeO83eSHYA1m/fSslFVbqtfKd82jWqW58eNvwPwzeqd\n3N2sJstXbfObfE8Mn8exE2cBCAoMJCU13atsBZExrEQwr836knta1PZJNl/n8/d93H3IHByOLIoF\nBXL9NZGcOZfiVTaA5g3y5iufJ9+JnHxb9tGyURVuq5c3X/lc6xz19H28/dEPztejV/n8vf/8PJ+v\nMxbEMewLRaxAUGAVgjnAG1rrclrrW7TWFYCxwDxfrDw5+RwREeHO7wMDAsnIyACgYsWK7N2zh5Mn\nTmC329mwfi12+3lCQ0M5euQID3a4l9GvjOLRxx43jbOysNlsAISGhpF0zvtyd0RYCGfO2Z3fZ2Y6\nCAw0XR0ZFsJZl2VJ51OJjAhx2yY7G0BSciolw0P8Kl/2L99m9SvxdPQdTP9gldf5fJ3xwNGT/Lzj\ngE9yFUQ+f9/HDkcWFW4szabPX+Sa0uFs333kyuZLTiUyvMRF21xXOpzWTauzKGG919n+cT5/779C\nyOfrjAVxDIsLFdSAIERrnevI1Fqv89XKw8LCSU5Odn7vyHIQFGSu+CNLlmTIsBEMen4Aw4cOombN\n2pQuXZpFC+fTvEVLEr78mk+XxDNq5HBSU1OxucwXOH8+mYiISK/zJSWnEBEa7Pw+IMDmvCd3NjmF\n8LCcX/gRocGcSbK7beNw5NzLiwgzj/WnfAAP3dOI2JHd6fzs25w4fc7rfAWR0df+v+3jg3+cpu6D\nY5jz2Y+8OTin9OtVPpcMAQEBufO55Mjuk4u16dy2AR9/tRGHwzfXbFdF//lxvoLI6I+ysgr+60oq\nqAHBVqXUXKXUw0qpKKXUQ0qpuYB3dVBLw4aN+OmHHwDYtnUL1apVdy7LyMhg12+/Mn/RYiZOnkZi\n4j4aNGxEZGQk4eERgBk0ZGRkkJmZSY0atfh5gxm7/PTjDzRq3MTrfGu37COqpSltNa17Czv2HHUu\n25V4jKoVrqN0ZCjFggJp0agq67cmum2zZddhWjWuBsA9LWqzevNev8rX/b5beTr6DqJ6T2P/kZNe\nZyuIjAXh/9M+/nRqX6pUuA6Ac8mpPjnprt2yj6gWtS4/37bEi7Zpc5vim9W/ep0rVz5/7z8/zufr\njOLKKKh3GfQDOgEtgUjgLPAF4P1sH6BN23asXbuaJx7rTlZWFmPGvc6XXyRw/vx5Hno4GoDohzoT\nHBzME08+RenSZXj8iR68MmokPR5/lPT0dAY8N5DQ0FAGvzCMMa+MInbqZCpVrky7e6K8zhe/citt\nmtVg1fxB2Gw2+rzyPtHtmxAWGszcJasZNmkJCTOfwWazsTB+HUePn8m3DcDwyUuZ+fIjFC8WxK59\nx1jy381+ky8gwMakFx7i0LHTfDSpNwA/bvydcbO+9JuMBeX/yz4GmDTvG2aP/hdp6ZmcT0mj35jF\n3udbtY02zRSr5g3EZoM+r35AdPvGVr41DJu8jIQZMdgCAnLy5dMmW7WKZUk87LsBqd/3n5/n83VG\nf1XUPofA5u3bmwpIVkpGYUdwLyQISjTsX9gx3LJvjpN8XrBvjgP8dx/7ez6w9nGjZws7hlv2TbH+\n339+ng/89zVo5bNd6nHe2vuXvcBPoFXKlijw55FNPphICCGEEFfnBxMJIYQQhc0v6+tekAqBEEII\nIaRCIIQQQnjCP6fgeU4qBEIIIYSQCoEQQgjhiaL2tkOpEAghhBBCKgRCCCGER4pWgUAqBEIIIYSQ\nCoEQQgjhkSJWIJAKgRBCCCGkQiCEEEJ4RD6HQAghhBBFjlQIhBBCCA/I5xAIIYQQosixZfnnTRC/\nDCWEEOKqYSvoDfx2NLnAz1U1bwor8OeRTSoEQgghhPDfOQQlGvYv7Ahu2TfHkZJR2CncCwny//7z\n93zgv33o7/nA2seNni3sGG7ZN8X6f/81fq6wY7hl3zgN8N/XYPYxUtCKWinbbwcEQgghhHBPKRUA\nzATqA6lAL631HpfljwGDgUxgrtb67YutT24ZCCGEEB7Iyir4r0voBIRorW8HhgOT8ix/C2gLtAAG\nK6VKX2xlMiAQQgghPJB1Bf67hJbAVwBa63VAkzzLtwElgRDMJMuLrlAGBEIIIcTVKRI44/J9plLK\ndSrADmAjsBP4Qmv998VWJgMCIYQQwhNZV+Dr4s4CES7fB2itMwCUUvWA+4FKwC1AWaVUt4utTAYE\nQgghxNVpNXAfgFKqGbDdZdkZwA7YtdaZwF/ARecQyLsMhBBCCA/4wdsOlwLtlFJrMHMEnlJKPQqE\na63fVUq9A/yklEoD9gLzL7YyGRAIIYQQVyGttQN4Os+Pd7ksnwXMutz1yYBACCGE8IB/fvK/52QO\ngRBCCCGkQiCEEEJ4Qv78sRBCCCGKHKkQCCGEEJ4oWgUCqRAIIYQQ4iqtENhsNqaNjKZe9ZtJTcsg\nZswH7Dt0wrn8vjvqMLLPvWRkOliwbC3zlq5x26Zy+WuZPfpxsrKy2Ln3D55/4xOyvJw66nA4eG3s\nq+zWmuLFi/PK6HFUqFjRuTxh+TIWzHuP8PAIOnbqTJeu3YhfuoTl8UsBSE1NRe/6je++X016ejpj\nXnmJs2fP4sjMZNwbEyhfoYJX+XzZf9mi2zch5pE7af1k3r+tUfgZa1S+gRkvPYLNBnsOHidmzGIy\nMx1+k68gXoMFsY8nDO7C7gN/Meezn7zK5sw3olvOtsZ+eGG+3lEmX/w65i1de8k20e0bE9P9Dlr3\nmOKbfP7ef8O7Ua/6TVZffMS+wy75WtVmZO/2ZGRmsmD5euYtXetcdmudiowb8ABRfXP/ieAJgzqb\nfJ+v9jqfM6MfHyO+UPgJfOuqrBB0vKseIcWDaP3kJEbFxjN+UBfnsqCgACYM7kqHmDja9ZxKz64t\nKFsmwm2bNwd35dUZX9C251RsNhsPtK7rdb6V3/2XtNQ0Fi3+mOcGDmbSxPHOZadPn2Lm9Fjem7eI\nuQve58svEjhy5DAPdu7Ce/MX8d78RdSqVZthI14iMjKSqZMmcl+HB5i38AOeefZ5EhP3eZ3Pl/0H\nUF+V48lOt2PzOlnBZBzT/wFejltOm6fMieL+O+r4Vb6CeA36Mt+1pcNZFhfD/Xd6nysnX11Cihej\ndY8pjJqewPiBnfPk60yHfjNp1yuWnl2aW/nctzGvwWbYbL55Ffp9/7WuS0hwEK2fmmr1Rac8+TrT\n4ZmZtOs9nZ6dTf8BDHqiDTNHdSckuJjz8deWCmNZbF/uv9P74yJXRj8/RsSFrsoBQfOGVfh2zW8A\nbNi+n8a1cq6Ya1S6gb2HjvN3kp30jEzWbN5Ly0ZV3bZpVLM8P278HYBvVu/krttqeJ1v86aNNG/Z\nCoB69Ruwc+cO57LDhw5TXSlKlipFQEAAtevUZdvWrc7lO3dsZ+/ePTz0cDQAWzZv4s9jf9KnZw++\nXJFAk1ubep3Pl/1XpmQYowc8wNC3Pvc6V0Fl7D5kDqs37aVYUCDXXxPJmXMpfpWvIF6DvswXViKY\n12Z9yeIVP3udy5mvQd5tlc+T70ROvi37aNmoits2ZUqGMrp/B4a+tcR3+fy+/yrnbGvHgdz9d0v+\n/Qew7/BJug+Zm2tdYaHBvPbuVz7NB/5/jPiCH/z5Y5+6KgcEEWEhnDlnd36fmekgMNA8lciwEM66\nLEs6n0pkRIjbNq5XFEnJqZQMD/E6X3LyOSIiwp3fBwYEkpGRAUDFihXZu2cPJ0+cwG63s2H9Wuz2\n887Hzpn9Dn1jnnF+f/ToESJLRvLue/O54YYbmffebK/z+ar/ihcLYtYrjzJs0hKSkr0/yRZExsDA\nAByOLCrcWJpNn7/INaXD2b77iF/lK4jXoC/zHTh6kp93HPA6k8f5klOJDC/h/jX48qMMm7yUpOTU\nwslXGP0XHpJrYJvpyMrJF543XwqR1mtq2cqtpGdk5lrXgaOnfJ4P/P8YERcqkDkESqlVQHCeH9uA\nLK11c2/Xn5ScQkRozuoDAmzOe8Jnk1MID8t5sUSEBnMmye62jcORcy85Isw81lthYeEkJyc7v3dk\nOQgKMl0dWbIkQ4aNYNDzAyhVqhQ1a9amdGnz9ybOnj3L/sREmt7WzNm2ZMlStL6rDQB33tWGuGne\n3x/1Vf/Vq34zVSqUJXZkd0KKB1Gj8g1MHNLVJ9UCX+5jgIN/nKbug2Po0fl23hzchd4vL/KbfAXx\nGvR1//laUnIKES4ZAgICcudzyZHdJ/m1qVf9JqpUuI7YEQ8TElyMGpVuYOKQLl5XC/y+/86lEBHm\nsi2bS75zKYSHuuYL8clr6h9n9PNjxBfkcwguz3AgHHgceMT66m7932trt+wjqmVtAJrWvYUde446\nl+1KPEbVCtdROjKUYkGBtGhUlfVbE9222bLrMK0aVwPgnha1Wb15r9f5GjZsxE8//ADAtq1bqFat\nunNZRkYGu377lfmLFjNx8jQSE/fRoGEjADb98jO3Nbs997oaNebHH753Lq9StarX+XzVf7/sPEDj\nh14jqvcxPrAaAAAgAElEQVQ0Hh8+j137jvns1oEv9/GnU/tSpcJ1AJxLTsXh8P4g9vfXoC/zFYS1\nW/YR1aLW5efblphvm192HqRxtzeI6jOdx4fPZ1fiMZ/cOvD7/tuamNMXdSrmzrc/b74qrN+2v8Cy\nuM3o58eIuFCBVAi01uuVUouAelrrpb5ef/zKrbRpVoNV8wdhs9no88r7RLdvQlhoMHOXrGbYpCUk\nzHwGm83Gwvh1HD1+Jt82AMMnL2Xmy49QvFgQu/YdY8l/N3udr03bdqxdu5onHutOVlYWY8a9zpdf\nJHD+/Hnn3IDohzoTHBzME08+RenSZQDYvz+RcuXK5VrX4BeGMfrll/j0448IDw9n/ATvZ/H7sv8K\nii8zTpr3DbNH/4u09EzOp6TRb8xiv8pXEK9Bf9/H8au20aaZYtW8gdhs0OfVD4hu39jKt4Zhk5eR\nMCMGW0BATr582hRYvquh/25TrJr7vOmL0YtN/5Uoztylaxk2eSkJcTHYAnLyXWn+foz4RNEqEGDz\nh7du5COrRMP+hZ3BLfvmOFIyCjuFeyFB4O/95+/5wH/70N/zgbWPGz1b2DHcsm+K9f/+a/xcYcdw\ny75xGuC/r0HrGPHlG5/ytWn/2QI/gTa6JbLAn0e2q/JzCIQQQojC5peX016QAYEQQgjhAf8ssHvu\nqnzboRBCCCF8SyoEQgghhAfkbYdCCCGEKHKkQiCEEEJ4omgVCKRCIIQQQgipEAghhBAeKWIFAqkQ\nCCGEEEIqBEIIIYRH5HMIhBBCCFHkSIVACCGE8IB8DoEQQgghihypEAghhBCeKFoFAqkQCCGEEAJs\nWf45TdIvQwkhhLhq2Ap6A2v3/F3g56rbq5Yq8OeRTSoEQgghhPDfOQQlGj9X2BHcsm+c5vf5Dp1K\nLewYbpUvE0yJpkMKO4Zb9g1vAf77GrRvnAZAiUbPFnIS9+ybYinRZGBhx3DL/ssU/8/XclRhx3DL\n/tNYAEo07F/ISfJn3xx3Rbbj8M8Ku8ekQiCEEEII/60QCCGEEP6saNUHpEIghBBCCKRCIIQQQnik\niE0hkAqBEEIIIaRCIIQQQnikqP0tAxkQCCGEEB5wFK3xgNwyEEIIIYRUCIQQQgiPFLVbBlIhEEII\nIYRUCIQQQghPyNsOhRBCCFHkSIVACCGE8IDMIRBCCCFEkXNVVghsNhvThnejXvWbSE3LIGbsR+w7\nfMK5/L5WtRnZuz0ZmZksWL6eeUvXum3ToEY5po94mNT0DLbpIwx+awlZXt4Y8vd8DoeD2ImvsXeP\nplix4gwe8So3l6/gXP7d1yv4dPECAgIDad+hEx27RPP1ini+XhEPQFpaKnt/13z6xUrS09OZPH40\n55LO4sh0MOzl17ipXHmv8oHVh8O6UK/ajaSmZRLz2ifsO3zSufy+lrUY2aud1Yc/My9+vXPZrbUr\nMK7//UTFvA1AA3Uz04d3NX24+yiDJ8X71T6uV/1mpo98mIxMB78f+IuYsR/5Jt+IbtSrfrO1rQ/Z\nd8gl3x11GNk7ioxMBwvi1+Xku0ib6PaNiel+B617TPEqmzPf8IeoV+0mUtMziBn78YX91+sek2/5\neuYtW+dcdmvtCox79gGi+s4AoEal65nx4sPYbDb2HDxOzLiPycx0+E2++upmlkzpxR6rL2d/tprP\nvt3ifb7BHahX9QZS0zOJGb+MfUdO5eRroRjZo7XJt2IT8xI2OpddVyqMNe/FcP/A+ew+eILrSoUx\nY9iDlI4oQWCAjZ7jPifx6Gmv8jkzjozOeT2N+eDC12Cfe03GZWuZt3SN2zY1Kt/AjJcewWbD7OMx\ni73ex74gn0PgBzq2rktIcBCtn5rKqOkJjB/YybksKCiACYM70+GZmbTrPZ2enZtTtkyE2zZxL0Yz\ndNIS2vaK5cw5O9HtGxf5fKt/WElaWirTZ79Pr37PMWv6W7mWvzN9EhNiZzPtnYV8tnghSWfPEnX/\ng0yeOZfJM+dSXdXimYHDCY+IZPaMKdx9z/1MeXs+T/Xtz8EDiV7nA+h4Z21CigfRumcco2asYPxz\nDziXBQUGMGFgRzoMeJd2fd+mZ+dmlC0TDsCgx1sz88VuhBTPGevGjXyIoZPjadtnJmfOpRAd1dD7\nfD7cxy/2ac/rs7/m7p7TCC4exL0ta3mf7666hBQvRuseU6xtdb4wX7+ZtOsVS88uVr6LtKmvyvFk\np2bYbDavswF0bF3H7N9/T2PU9C8YP7BjTr7AACYMepAO/WfRrk8cPTvfnrN/n2jDzFHRufbvmGfu\n5+UZK2jTMxaA+1vV9qt8DWuUI/aD74nqO4OovjO8HgwAdGxV0+R7ejajZn3D+P7tc+cbcC8dBi2g\nXf+59OzYhLKlw5zL4l7oiD0t3fn41/rdw8ffbqNd//d4dfZ3qIrXeZ0PoONd9UzGJycxKjae8YO6\n5GQMCmDC4K50iImjXc+p9OzawnoN5t9mTP8HeDluOW2eMoPR+++o45OMIrcrNiBQSgX7al3NG1Tm\n2zW/AbBhxwEa18q5Iq1xyw3sPXSCv5PspGdksmbLPlo2quK2zc1lS7Fu234A1m5NpHmDykU+346t\nm7m1WQsAatWpz+7ffs21vFLV6iQnJ5GWlkoWWbieA/RvO9mfuJcOnR4CYOe2LZw4/idDB/Tmu69X\nUL9RE6/zATRvUIlv12oANuw4SOOaLn1Y6Xr2Hnbpw62JtGxo+mXf4ZN0H7Yg17puLluSddsPALB2\n636aN6jkg3y+28db9GFKR4YCEB4aTHpGpg/yVcnZ1vb9ufNVcpcv/zZlSoYyun8Hhr61xOtcOfkq\n8+3aXWZbOw5cuH8P5d2/VQDYd/gE3YfOy7Wu7i/MY/XmfRQLCuT6ayI4c87uV/ka1ixP+5a1+Pbd\n/rw9KprwUO9/FTavV4Fv1+8x+XYepnGNm3Py3XIde4+c4u+kFJNv20FaNrgFgPH92zN72c/8cSLJ\n+fjb61bk5usiWTG1B93vqccPm30zqG/eMO/rKacKaV6Dx3P6cPNeWjaq6rZN9yFzWL1pr7WPIzlz\nLsUnGb2VdQX+u5J8PiBQSj2glDqglNqjlIp2WfQfX20jIjwk1wsi05FFYKB5KpHhIZx1+YWQdD6F\nyPAQt232HzlJy0bmYL7vjjqElShe5POdTz5HWHi48/uAwAAyMzKc31eqXJV+PbrT69HONGtxB+ER\nkc5lHy6YwxM9n3Z+f+yPo4RHRDJx+mzKXn8jHy/K/cvQUxFhefvDkdOHYcGcdVmWlJxKZHgIAMtW\nbb/ghLr/yCnngOG+VrUIC/Gvfbz34HEmDe3Cls9Hcv01EfywcY/3+cJCcp0YMzNd+y9PvuRUIsNL\n5NumeLEgZr38KMMmLyUpOdXrXG7zufZfWEie/ZuSs39Xbrtg/zocWVS4oTSbPhnGNaXC2f77Ub/K\n98vOg4yctpx2feJIPHKSF3tH+SBfMGeSL/P4OJ9KZFgI/7q3Icf/Tua/G3K/vireWIrTSXbuf34+\nh/48w+DHWnmdz2T8B6/B86lERoS4beNwZFHhxtJs+vxFrikdzvbdR3ySUeRWEBWCF4EGwG1AX6XU\nk9bPfVNrBJLOpRARljPKDrDZnPeTzp5LITw0xLksIjSEM0l2t236jF7M0Kfa8eXbz3D8VBIn/04u\n8vlCw8I5n3ze+X2Ww0FgkClx7tuzm/Wrf2DRkv/w/pKv+Pv0Kb7/7hsAziWd5dDB/TRo3NTZNrJk\nSW5v1RqAZi3vZPeunV7nA/NL1m0fJqfmusqKCAvmTJL7K4Y+Yz5maI82fDmjL8dPn+PkGf/axxOH\ndKFtr1gadH2dD774OdftB4/zJacQEZaTISAgwKX/UvLpP3u+bepVv4kqFa4jdsTDLBrfgxqVbmDi\nkJzSr1f5XPoo9/5NITzMNV/IJa8IDx47Td0urzPn89W86av+81G+5au2sXnXYevf26mvbnb72MvP\nl0pE6GUeH6HBnDmXwpP3N+LuJlX4evq/qVf1Bt57qSvXlwnn5JnzrPjJVEO+XK1pVMP7fCZjSu6M\nAXn70PUYcXkNumlz8I/T1H1wDHM++5E3B3v/GvSFrKyC/7qSCmJAkKa1Pq21Pgk8CPRXSt0Fvqt9\nrN2aSFQLc5+1aZ2K7NiTc0Wwa/8xqla4jtKRoRQLCqRFoyqs37bfbZt7W9biqZcWcl/MDK4pGcZ3\n63WRz1e7XgM2rP0RgF93bKVSlWrOZWFh4RQPDiE4OITAwEBKlS5DUtJZALZt2UjDJrflWled+g3Z\nsMasa/uWjVSsVMXrfGBK+1HNawDQtE4Fduw95ly2K/FPqpa/ltKRJUwfNqjM+u373a7r3pY1eerl\nxdz3zDtcUzKU79bv9kE+3+3j02fPk2Rd7f1x4qzz9oFX+bbsy9lW3Vty50vMm68q67cl5tvml50H\nadztDaL6TOfx4fPZlXjMJ7cOTF/UNNuqU5Ede/5wyfcnVcu75GtYmfXWbbP8fDq5J1XKXwvAufOp\nOBzeTzbzZb6EuKdpUtuUvu9qWt05OPAq3/aDRDUzx23T2uXYse/PnHz7j1O13DWUjsg+PiqyfsdB\n2vV/j3sGzCVqwFy27TlGz3Gf8+epc6zddpCo26sD0LJBRX5L/MvrfGC9Blua+RyX9Rrcmui2zadT\n+1KlgpnbcC45FUdRm83nJwriXQb7lVKTgVFa6ySlVBfga6CUrzYQv2obbW5TrJr7PDYb9Bm9mOj2\njQkrUZy5S9cybPJSEuJisAXYWBi/jqPHz+TbBsyM1S/ffgZ7Sjrf//I7X6/+9RJbv/rztbzzbjZt\nWMezvR8niyyGvjiW775egd1up0Onh+jQ6SGe7/skQcWKcdPN5Yi6/0EADh3Yz4035b56eHrAECa9\n8SoJSz4hLDyckaPf9DofQPz/dtDmtuqsmtPf9MeYj4mOamj6cNl6hk1NICG2DzabjYUJGzh6/Kzb\nde05eIIvZ/Q1fbhxD1+v2eV9Ph/u435jP2Lh60+SkekgLT2TfuM+8k2+ZopV8waabb36gckXGszc\nJWsYNnkZCTNisAUE5M6Xp01BiV+13fTFe89is9noM/pDoqMamXxL1zJsSjwJ0/ua/lu+nqPHz7hd\n16T53zH71UdJS8/gfEo6/cZ+7Ff5nn3jMya/0IX0jEz+PHmWZ177xPt8P/xGm1ursOrt3mZfvb6U\n6Hb1zOtv+S8Mi/sPCZOfMPlWbOKoy5yBvIbH/YeZwzvRp1NTziSn0GP0p17nA4hfuZU2zWqwav4g\n04evvE90+ybWa3A1wyYtIWHmM+YYzn4N5tMGYNK8b5g9+l+kpWdyPiWNfmMW+ySjt4raJxXavH17\nU15KqSDgX8AnWuvz1s+uB0ZorZ+/zNVklWj8nE9z+ZJ94zT8Pd+hU7673+tr5csEU6LpkMKO4ZZ9\ng3nXhb/uY/vGaQCUaPRsISdxz74plhJNBhZ2DLfsv0zx/3wtRxV2DLfsP40FoETD/oWcJH/2zXHg\nw9vU7ny183iBDwna176uwJ9HNp9XCLTWGcD8PD/7E7jcwYAQQgjh9xzySYVCCCGEKGquyk8qFEII\nIQpbUZtDIBUCIYQQQkiFQAghhPBEUftrhzIgEEIIITwgtwyEEEIIUeRIhUAIIYTwgLztUAghhBBF\njlQIhBBCCA/IHAIhhBBCFDlSIRBCCCE8UMQKBFIhEEIIIYRUCIQQQgiP+PqvBRc2qRAIIYQQQioE\nQgghhCcchR3Ax2x+WvLwy1BCCCGuGraC3sBnW/8o8HPVQ/VvLPDnkc1vKwQlGj1b2BHcsm+KpUTD\n/oUdwy375jhK3DqosGO4Zf95Mn8lpRd2DLfKRhQD8Nt9bN8cB0CJpkMKOYl79g1v+f8x3Pi5wo7h\nln3jNErcNrSwY7hlXz8RwG/70L5x2hXZjp9eUHtM5hAIIYQQwn8rBEIIIYQ/K1r1AakQCCGEEAKp\nEAghhBAekTkEQgghhChypEIghBBCeKCofQ6BVAiEEEIIIRUCIYQQwhNFbQ6BDAiEEEIIDxSx8YDc\nMhBCCCGEVAiEEEIIjxSxAoFUCIQQQgghFQIhhBDCI44iNolAKgRCCCGEuDorBDabjWkjulGv+s2k\npmUQM/ZD9h064Vx+3x11GNk7ioxMBwvi1zFv6dpLtolu35iY7nfQuscU3+QbGZ2zrTEfXJivz70m\n37K1zFu6xm2byuWvZfbox8nKymLn3j94/o1PvH6ri81mY9qwrtSrdhOp6RnEjPuEfYdd8rWqxche\n95CR4WBBwgbmLVvnXHZr7QqMG9CBqKdnArDwtce5/poIACreWIYNOw7wxIuLvMoH4HA4mDx+LHt+\n302xYsUYNmoM5cpXcC7/asVyPlw0j/DwCO7t8CAdOnV1Ltu5YxuzYicz/d35ABw+dJDXX30Rm81G\npSpVGTTsJQICvBsL+3If16t+M5OHdSPTkUVqWga9Ri3kr1NJ3ucb1oV61W4kNS2TmNc+Yd/hkzn5\nWtZiZK92ZGRmsmD5z8yLX09QYADvjIqm4k2lCS4WxPi5/2XFj78620wY2JHdB44zZ8lar7I58/no\nGK6vyrFkWh/2HDwOwOzPfuKzbzZ7n294N+pVv8na1kd5jpHajOzd3uq/9cxbmtMnt9apyLgBDxDV\n1/yZ6srlrmX26MdyjuHxn/nmGH6hszmG0zKIef3TPPu3JiN7tjP9l7CBefEbrP37MBVvtPbvvO9y\n7d/oexoQ83BLWveK8ypbroz/sA/dtalX/Wamj3yYjEwHvx/4i5ixH/nFW/4KP4FvXZUVgo531SWk\neDFa95jCqOkJjB/Y2bksKCiACYM706HfTNr1iqVnl+aULRNx0Tb1VTme7NQMm83mo3z1CCkeROsn\nJzEqNp7xg7rkydeVDjFxtOs5lZ5dW1j58m/z5uCuvDrjC9r2nIrNZuOB1nW9z9e6DiHBQbTuGcuo\nuBWMf75jTr7AACYM7ESH/u/Qru8MenZuRtky4QAMevwuZr4UTUjxYs7HP/HiIqKenkn00Hn8fc7O\nC5OXeZ0P4Mf/fUdqWhqz5n3A0wMGMmPKROeyv/8+zXuz4pj+znymvzufb79awR9HjwDwwYK5TBj7\nCmlpac7Hx02eQO+YAcyYsxCysvjp+5Ve5/PlPn7rhYcY9OanRPWeRvzKLQx+qp33+e6sbbbVM45R\nM1Yw/rkHcvIFBjBhYEc6DHiXdn3fdu7jR+5tzKkzybTtM5OOz81mylBzjFxbKoxlU3txf6taXudy\n5vPhMdywZnli319FVJ/pRPWZ7vVgAKBj67rmGHlqqrWtThfme2Ym7XpPp2dnkw9g0BNtmDmqOyHB\nOcfIm4M68erMFbTtFYsNHx3Dd9Y2fdErjlEzv7xw/z7fkQ7Pzqbd02/Ts1P2/m3EqTPnadv3bTo+\nP4cpQ3KeU/3qN/Fkx6b46FegyehBH7pr82Kf9rw++2vu7jmN4OJB3NvSd69FkeOKDAiUUiWUUsG+\nWl/zBlX4ds1vAGzYvp/Gtco7l9WodAN7D53g7yQ76RmZrNmyj5aNqrhtU6ZkKKP7d2DoW0t8FY/m\nDfNuK+fK1uQ7npNv815aNqrqtk2jmuX5cePvAHyzeid33VbD+3z1K/Html1mWzsO0Lima/9dz97D\nrv2XSMuGVQDYd/gk3V+Yl+86R/Vpz9sf/8Sxk95d2WbbtmUzt93eAoDadeuz67edzmVHDx+majVF\nZMmSBAQEUKNWHXZu3wrAzeXKM27i1Fzr0rt+pUHjWwG4rXkrftmwDm/5ch8/MXwe23abAU1QYCAp\nqene52tQiW/XarOtHQcvvo+3JtKyYWWWfLeV0e98DZiru4xM88GsYaHBvDb7Gxb/Z5PXuXLy+e4Y\nblizPO1b1ebbOc/y9suPEB7q/a+a5g0q52xrx4Hc+W7JPx9Yx8iQubnWZY7hPQB8s+ZX7mpa3ft8\n9Svx7brsY/ggjWuUy8lX6Xr2Hj6Ze/82qMyS77bl7F9y9m+ZyFBGx9zL0CnLvc6VK6MHfeiuzRZ9\nmNKRoQCEhwaTnpHp06yeysrKKvCvK6lABgRKqVpKqWVKqXlKqbbAb8CvSqkOvlh/RFgIZ87Znd9n\nZjoIDDRPJTIshLMuy5KSU4kML5Fvm+LFgpj18qMMm7yUpORUX0T75/nOpxIZEeK2jWvVIik5lZLh\nIb7Jl5ySsy3HJfJZ21y2alu+B+J1pcNp3bQai77Y4HW2bMnJ5wgPj3B+HxAQQEZGBgDlK1Qgcd8e\nTp08QUqKnY0/ryPFbjK3vrsdQUG574RlZWU5+zE0LIxz57wftPhyHx87cRaAZvUr8XT0HUz/YJWP\n8rnbx8GcdVlmjpEQku1pnDufSnhoMIvfeILRs74C4MDRU/y886DXmS7M5/0xHBgYwC87DzByajzt\nesWSeOQkL/Zp732+8Lz9l5WTLzzv/k3JOUZWbr3gGMl1DJ9PpWR4Ce/zhQVfYv9eeAzn2r/jH2f0\nrK8ICLAx66VuDJuWQNJ53/0OBM/60F2bvQePM2loF7Z8PpLrr4ngB2uAJXyroCoEs4ApwP+Az4Cm\nQENghC9WnpScQkRYzokxICCATGu0ezY5JdcVQkRYMGeS7Pm2qVf9JqpUuI7YEQ+zaHwPalS6gYlD\nckq/XuVzyRAQYMudzyVHRKhLvnzaOBw5fz4j+7n4PJ8tT77QC/NdTOe76/HxV5twOHw3mg0LC+f8\n+WTn91lZWc4TfURkSQYMGsZLLwxk9MgXqF6jFiVLlXa7Ltf5AueTk4kIj/Q6ny/3McBD9zQidmR3\nOj/7NidOn/NNvjB3+zg1n2PE/BIuV7YkX739NIv/s5GPv/a+9H7xfN4fw5mZDpav3Mbm3w4BsHzl\nNuq7XC17nO/cRfrvXN5jJOSix4jrcWFeC+e9z5ecepHXXyrhLtkjQoOdA6lyZUvy1cy+LP7PJj7+\nZguNapSjSvlriX2hC4vGPUaNStczcWBHfMGTPnTXZuKQLrTtFUuDrq/zwRc/57r9UJgcV+DrSiqo\nAUGA1vp7rfUCYJnW+i+t9VkgwxcrX7tlH1EtzD2kpnVvYceeo85luxKPUbXCdZSODKVYUCAtGlVl\n/bbEfNv8svMgjbu9QVSf6Tw+fD67Eo/55NbB2i37iGpZ+/LzbU1022bLrsO0alwNgHta1Gb15r3e\n59u6n6gWNc226lRkx94/XPL9SdXy1+bka1iZ9dsPXHR9bZpW5xurzOcrdes3ZO3qHwHYuX0rlatW\ncy7LyMhg965fmTFnIaPHT+Lg/kTqNmjodl3VVA02/2KqF+vX/Ei9ho28zufLfdz9vlt5OvoOonpP\nY/+RkxduzJN8W/cT1dzcXmpapwI79h5zyZe9j0uYfA0qs377fsqWCSdheh9eilvBwoSffZLDbT4f\nHcMACTNiaFLb3H65q2l15+DAq3xbE3O2Vadi7nz78+arwvpt+92ua4s+TKvGVQG4p3ktVm/e532+\nbfuJap59DFdgx56L7F/rGC5bJpyE2N68FPelc//+8ushGj8yiah+s3j8pQ/Ylfinz24deNKH7tqc\nPnueJKuq+ceJs87bB8K3CupdBlopNQfoo7XuAaCUGg4cu2iryxS/ahttmilWzRuIzQZ9Xv2A6PaN\nCQsNZu6SNQybvIyEGTHYAgJYGL+Oo8fP5NumoMSv3EqbZjVYNX8QNpuNPq+8T3T7Jla+1QybtISE\nmc9gs9ly8uXTBmD45KXMfPkRihcLYte+Yyz5r/dXbfH/206b26qz6r0B2LDRZ8xHREc1Iiy0OHOX\nrmPY1HgSpvcx+RI2cPT4mYuur1rFsiT66ESW7Y677uaX9WuI+fdjZGXBiFfG8u1XK7CfP0/HLt0A\n6PlYN4oHBxP92JOUukiF4JnnhzLhtVfJmDGNirdUovXd93idz1f7OCDAxqQXHuLQsdN8NKk3AD9u\n/J1xs770Lt//dph9PKe/eb2P+ZjoqIaElSjO3GXrGTY1gYRY1318lrcGPUipyBKM+Hc7RvzbTGx8\n8PnZpKT6ZByfO58Pj+Fn3/iEyS88RHpGJn+ePMsz4z72Tb7bFKvmPm+2NXqxyVeiOHOXrmXY5KUk\nxMVgC8jZv+4Mn7KMmS91p3ixQHYl/smS77Z4n+9/O2jTtBqrZpvXWJ+xHxN9TwPTf9n7d1pvky/h\nZ2v/dqRUZCgj/t2WEf9uC8CDA+cUyP4Fz/owvzYA/cZ+xMLXnyQj00Faeib9xn1UIJn/qcJ+o4NS\nKgCYCdQHUoFeWus9LstvBSYDNsz5919a65T81gVgK4hJC1bIB7TW8S4/+xewRGt9OfWyrBKNnvV5\nLl+xb4qlRMP+hR3DLfvmOErcOqiwY7hl/3kyfyV5P3GuoJSNMDPE/XUf2zebt4WVaDqkkJO4Z9/w\nFn5/DDd+rrBjuGXfOI0Stw0t7Bhu2debd/34ax/aN04DcxIsUO9tOFjgQ4KeTSu4fR5KqS5AR611\nD6VUM2CE1vpBa5kN2Aw8pLXeo5TqBfyotdbu1lcgFQKttQOIz/Oz9wtiW0IIIURh8INPKmwJfAWg\ntV6nlGrisqw6cBIYqJSqA6y42GAArtLPIRBCCCEEkYDr/apMpVT2hf61QHMgDmgL3K2UanOxlcmA\nQAghhPBAVlbBf13CWSDC5fsArXX2pJCTwB6t9W9a63RMJaFJ3hW4kgGBEEIIcXVaDdwHYM0h2O6y\nbB8QrpSqan3fCtjJRVyVf8tACCGEKGx+MIdgKdBOKbUGM4nyKaXUo0C41vpdpVRPYLE1wXCN1nrF\nxVYmAwIhhBDiKmRN4H86z493uSxfiflgwMsiAwIhhBDCAz78cFa/IAMCIYQQwgOFf8fAt2RSoRBC\nCCGkQiCEEEJ4wkHRKhFIhUAIIYQQUiEQQgghPCFzCIQQQghR5EiFQAghhPBAUXvboVQIhBBCCCEV\nAiGEEMITfvDRxT5ly/LPJ+SXoYQQQlw1bAW9gck/7Cvwc9WgOyoX+PPI5rcVghIN+xd2BLfsm+P8\nP++RKGgAACAASURBVF+jZws7hlv2TbF+nw/gVHJmISfJX5mwQOAqOEYaP1fYMdyyb5wm/ecF+8Zp\nAJRoPrKQk+TPvub1K7Id/7ye9pzMIRBCCCGE/1YIhBBCCH8m7zIQQgghRJEjFQIhhBDCA346Kd9j\nUiEQQgghhFQIhBBCCE/IHAIhhBBCFDlSIRBCCCE8IBUCIYQQQhQ5UiEQQgghPJBVxD5lXyoEQggh\nhHBfIVBKvXyxhlrrMb6PI4QQQlwditocgovdMrhif2FJCCGEuNoUsc8lcj8g0FqPzv63UioMqALs\nAEporZOvQDYhhBBCXCGXnFSolGoDvAsEAs2BbUqpx7TW3xR0OHdsNhvTRkZTr/rNpKZlEDPmA/Yd\nOuFcft8ddRjZ514yMh0sWLaWeUvXXLLNhMFd2H3gL+Z89pNf5atX/WYmD+tGpiOL1LQMeo1ayF+n\nkrzPN6JbzrbGfnhhvt5RJl/8OuYtXeu2TX1VjiXT+rDn4HEAZn/2E599s9mrfFdDRofDwcQ3xrBn\nt6ZY8eKMGDWG8hUqOpf/54vlfLBwLuHh4dzXsTMdO3UlIz2d10a/xB9Hj5CWns5TvfrS6s427Na/\nMeG10QQGBVG+QkVGvjyWgADvpvf48jVYufy1zB79OFlZWezc+wfPv/GJ1x/ZarPZmDa8G/Wq32Tt\nq4/Yd9glX6vajOzdnozMTBYsX5+zf/Np06BGOaaPeJjU9Ay26SMMfmuJb/L5qP9qVL6BGS89gs0G\new4eJ2bMYjIzHd7n+4f9l+3WOhUZN+ABovrGAVC53LXMHv1Yzv4d/5lPPpLXZrMxbUhH6lW70WR8\nYwn7jpzKydiiBiP/3cb04Re/MG/5LwQE2Jg5vDPVK1xHVlYWAybG8+u+P1k4pjvXlwkHoOKNpdmw\n8xBPvPyR1xm95ShiJYLL+a3zBtAS+Ftr/QdwJzCxQFNdQse76hFSPIjWT05iVGw84wd1cS4LCgpg\nwuCudIiJo13PqfTs2oKyZSLctrm2dDjL4mK4/866fpnvrRceYtCbnxLVexrxK7cw+Kl2PshXl5Di\nxWjdYwqjpicwfmDnPPk606HfTNr1iqVnl+ZWvvzbNKxZntj3VxHVZzpRfab7ZDBwNWT8YdV3pKWl\nMXvBh/QbMIjpUyY4l/19+jTvvh3LjNnzmTlnId98+QV/HD3CV18mEFmyFLPmvs+UuHeY9OY4AN57\ndyb/7hPDO3PfJz09jdU/fu91Pl++Bt8c3JVXZ3xB255TsdlsPNDa+2OlY+u6hAQH0fqpqda+6pQn\nX2c6PDOTdr2n07OztX/dtIl7MZqhk5bQtlcsZ87ZiW7f2Pt8Puy/Mf3/j737Dm+q7P84/k66J5Tl\nYM/DLFAUECoCggVB9nx8RLBQLCgbWaJsGbJaKFMQVMQF1AoqKDzqD0rZWw6j7KFQoJQ2Lc34/ZGS\npoVAbVIa6vd1Xb0kntznfHKfO8md7zk5eY0PFnxP8z5zAWjTpKb9+XLRfwDDejUnanwPPD3cLPef\nMawDE6I20qJvBBocs38B2jWpbu6PsMWMX/Qz0we9mpnRRcvMwW1oO2QFLQcsI7R9fUoE+NImuCoA\nzd9ewoSlW5jQ3/x61+uDtYS8s5zuYz7nVpKO9+ZvdEhGkVVOJgRaVVWv3ruhquqxf7IBRVFK/ONU\nj9CobkW27PgTgF2Hz1KvehnLsqrln+b0hWvcStKRrjewY/9pgoMq2Wzj4+XB1MWbWLNxt1Pm6zV6\nJYdOXALA1cWF1LR0+/PVyb6t0tnyXc/MdyCe4KCKNtvUrVaaVi/WYMvyQSz6oCe+3h5253sSMh48\nsI+GjYIBqBlYmz+PHbUsu3TpApWrKBQqVBitVku1GjU5cvggzVuGEDZgkPlOJnBxMRfoqijVuJ2Y\niMlkIiU5BVdX+78N7MgxGFStNH/sPQnA5u1Hadagqv356lTI3NaRc1n3bzlb+/fBbUqWKMzOQ2cB\niD14hkZ1Ktifz4H912PEcrbvO42bqwtPFfUn8U6q/fly0X8A8RcT6DFiRZZ1mffvKQA27zhGs/pV\n7M4H0Kh2WbbEmcfNrqMXqFe1pFXGEpy+mMCtpFRzxoNnCa5Tjpjf/2TgjA0AlHm6MIlJWftqfN8W\nLPo2lqsJ9lVJHcVoyvu/xyknE4KLiqK0BUyKohRWFGUccN7WnRVFqWL9B3xv9W+H8PPxJPGOznLb\nYDDi4mJ+KP4+nty2WpaUkoa/n6fNNucuJ7D7yDlHRXN4vqvXbwPQsHZ53u7ehMgvtj3efMlp+Pt6\n2Wyz5+g5xs6LpmXfCM5cSmBcWCu78z0JGZOT7+Dr62u57eKiRa/XA1C6TFniT5/iRsJ1UnU69uza\niU6nw9vbBx8fH5KTkxn73hDL5KB0mbLMmTWNHp3bcuPGdYKeq293PkeOQY0m8/zipOQ0Cvl62p/P\n1zPLG6PBaMrM55s9Xyr+vp4225y9lGB5w3u1SU18vNztz+fA/jMaTZR5JoB9342jaIAvhzMm+Hbl\ny0X/AWzYepB0vSHLurLs35Q0Cvl62Z0PwM/bI2tGg1VGHw9uWy1LSrlryWgwGFn2fhfmDHuNtZsP\nWO5TPMCHpvUq8tmmfQ7JJ+6XkwlBf+B1oDQQD9QBwh5y/1+A74HFwBJAyfjvYruSWklKTsXP6lOe\nVquxHJO7nZyKr0/mC5aftweJSbqHtnE0R+fr8koQEWN70HHQIq7fvOOYfFYZtFpt1nxWOfx8rPI9\noM33Ww+x/88LAHy/9RC1q5ayO9+TkNHHx5fk5Mxza41Gk+WTvb9/IQYPH82YkUP4YOwIlKrVKVy4\nMAB/Xb3CO2G9afXqa4S0bgvAvFkfsfiTz/hq3UZat2lPxJyZ92/wH3LkGDQaM58n9/ra7nx3UvHz\nsdqWxirfnVR8va3zeZrz2WgTNnENI/u0ZNOigVy7kUTCLfvPeXb0c/j8lZvUaj+J5d/+wYzhmYcf\ncp0vF/1ni9HqY6j5saTYnQ/MkwvbfZiW9Tns7Z5l8tBvyrcEdp9D1OiOeHuaD290bFaTr7YczJI3\nv5lMef/3OD1yQqCq6t+qqvbE/C2Dkqqqds04l8CW54BjwEeqqjYDDqiq2kxV1eaOiQyxB+IJCa4B\nQP1a5Thy6rJl2fEzV6lUpjgB/t64ubrQOKgScQfPPLSNozkyX49Xn+ft7k0I6Tefs5cSHJevcfWc\n5zt0xmabmIXhPFfDXBptVr+K5Y23oGcMrFOX2O1/AHDk0EEqVqpsWabX6zlx/BiLP/mMKTPmcu5s\nPIG1g7iRcJ3BA/oxYNAwXuvQ2XJ/v0KF8PExVxuKFS9BUtJtu/M5cgweOH6RF+uZH98rjWuwff9p\n+/MdPJO5r2qWzZrvbPZ8FYk7dNZmm9bB1enz/mpeDV9I0UI+/Bqn2p/Pgf33zbz+VCxTHIA7yWkO\neUPLTf/ZckC9yIv1KgHwSqPqbN8fb3c+gNhD5wh5wVwYrl+jNEdOW448c/zs31QqXZQAPy9zxjrl\niTt8np6t6jDijZcASElNx2g0Wfqr+XOV2Bx7wiHZxIPl5FsGtYBVQJmM28eBN1VVfeCrgqqqfyuK\n0g34WFGU5x0Z9p7orQdp3rAq2z4dhkajIezDz+ne6jl8vD1YsW47o2avIyZqIBqNhtXRO7l8LfGB\nbfKKo/JptRpmv9eFC1dvsnZ2PwD+2HuSKYs32Zdv2yGaN1TYtnIoGg2ETfiC7q3qZeTbwag5G4hZ\nGI5Gq83M94A2AIM++po573UhXW/gr4TbDJzyld399yRkfKlZC3bt3EG/3v8Bk4lxE6by848/oEtJ\noUPnbgC8+Z/OeLh70PON3hQOCGDurGkkJSWycvliVi43F8zmRC5h7PhJjB8zAhcXF9zc3Bgz3v5r\nfjnyOTJ6znqiPuiJu5srx+Ovsu4X+0/KjN52iOYNFLatGGLeVxPXmPevlzsr1scyas56YhaEo9Fq\nsu7fbG3AfOb+pkUD0aWm89uek/y8/R+d5vTgfA7sv9krN7Ns4n+5m24gJfUuAyatsT9fLvrPltFz\nNxD1fg/c3Vw4fuYv1v16wOZ9/1HG347R/PlKbFvS39wfU7+je8va+Hi7syJ6N6MiNhEzr4+5D3/Y\ny+Xrt4n+31GWjuvClqh+uLm6MHL+RlLvmg/FVS5TjDOXbzxiq49XQfuWgeZRXy9RFGU7MEVV1R8z\nbncEhqiq+tKjVq4oSm+gT07um43Jq+47/7DJ46PbvwCnzxc0KL9j2KTbF+H0+QBuJBsecc/8UcTH\nBcD5x2C9wfkdwybd3vnSf3bQ7Z0PgFejsfmc5MF0O6bBY7i43rgfT+T5jGBq6yqP7SKBOTmHwOve\nZABAVdX1gH9OVq6q6qe5mAwIIYQQTq+gnUPwsN8yuPc9m4OKoowGPgH0mE8w/OMxZBNCCCHEY/Kw\ncwh+A0yYyy5NMX/b4B4T4Lw1XyGEECKP5c331PLPw37LoPzjDCKEEEKI/JOTbxkowADAF3O1wAUo\nr6pqkzzOJoQQQjitgvYtg5ycVPgVcAuoCxwASmD+1UMhhBBCFBA5/S2DD4GfgH1AB6BBnqYSQggh\nnFxB+5ZBTiYEKYqieAAngHqqqqYB9l/MXAghhBBOIyc/q/Y5EIP564axiqK0Auz/dQ4hhBDiCeZE\nP6vgEDn5LYMFQGdVVa9h/vrhUsyHDYQQQghRQDzswkQfZLttfbMWYP8F14UQQogn1KMu/f+kedgh\ng8d2/WQhhBDiSVPQDhk87MJEEx9nECGEEELkn5ycVCiEEEKIbApahSAnXzsUQgghRAEnFQIhhBAi\nFwraSYUaWw9IURQj5l81hPtPMDSpquqSh7kKVi8LIYR43PL8xPh31/+Z5+9VkR2rPbYT/B92UmG+\nHk7wqjc4Pzf/ULq98/Gq+05+x7BJt3+B8+drPC6/Y9ik2z4VcN4xqNs7H4CzCan5nMS2ckU98Xp+\nWH7HsEm3e47T7l/IeI1pNDa/Y9ik2zENcP7nSF771/z88T2KopTAfJXC7L922CuPswkhhBDiMcnJ\nOQTrgNNAQ2AD8ApwMC9DCSGEEM6uoJ1DkJPDAsVUVX0T8+8ZrMN8+eIaeRlKCCGEEI9XTiYENzP+\nqwK1VVVNBNzyLpIQQgjh/Arazx/n5JDBVkVRvgFGAJsVRQkCnPdsJiGEEEL8Yzn5tcNxwGhVVc8B\nPTFXCjrmdTAhhBDCmRlNpjz/e5xy8i2DXhn/bZzxvxKAlsDqPMwlhBBCiMcoJ4cMmln92w14Efgd\nmRAIIYT4FytgXzJ49IRAVdU+1rcVRSkCfJVniYQQQgjx2OXmtwzuAOUcnEMIIYR4ohS06xDk5ByC\nbWT9TYMKwKa8DCWEEEKIxysnFYIJVv82AddVVT2WN3GEEEKIJ0MBKxDkaELQRVXVd63/h6IoqzKu\nXiiEEEKIAsDmhEBRlOWYDw88pyiK9aWK3YBCeR1MCCGEcGaP+zoBee1hFYIpmE8enI/5sMG932TW\nA3/maapH0Gg0zB/dlcAqz5J2V0/45LXEX7xuWf7qizUY268VeoOBVd/HsXJ9rGXZ8zXLMuXd1wjp\nvwCACqWKsWzi65hMJo6evsKQ6d/afaKIRqNh/tjuBFYpac436QviL1jla1KTsWGt0RuMrNoQy8r1\nOx7ZZubwTpw49zfLv/0/u7I5Ol+F0sVYNvGNzP776GuHnGij0WiYP6IdgZWeNm9v+nriL93IzNi4\nKmP7NDNn/GEvK2P2WJYVL+zDjhUDaDNkJSfOW/XhoFc5cf46yzfsckw+B41BS75hHc37+Lvtducz\nGo1EfjyVMydP4ObuzpAxH1KyVBnL8q0/b+S7L1ej1brwStsOvNapGwBrV3/Czj/+R7o+ndc6daPV\na504d+Y082dMwmSCkqXLMHT0h7i45uZ85EwajYb5ozoTWPlZ0tL1hE/5Olv/VWds31fQ642sitnF\nyg07Lcuer1GGKe+2JeTtKAACqzxL5Oiu6A0GTp6/RvgU+8dgbvavrTaBVUoSObYbeoORk+f+Jnzy\nWsfkG9GOwMrPmLf10br7nx9vNc94fuxh5fd70Go1RI3uSJUyxTGZTLw7K5pj8X8RWPkZIke2N+e7\ncJ3wj9Y77jnsoD6sU7UUkWO6kZau55B6ieEfr3OKE/ryP4Fj2bxSoaqqZ1VV/R8QDNRSVfU34BQQ\nQj5furhd01p4erjStM88xkfGMH1oB8syV1ctM4d3pO3AKFr2iyS0YyNKFPEDYFiv5kSN74GnR+ZP\nMcwY1oEJURtp0TcCDRpea1rL/nzNAvF0d6Xpm7MZHxHN9GGdsuXrTNvwBbQMnUdo58aUKOJns02x\nAF82LAinzUv258qLfDOGd2bCwh9oEToPjcYx/QfQrkk18/b6L2H84s1Mf/fVzIwuWmYOepW2Q1fS\ncuByQts/T4kAH8uyBe91QJemt9y/WGFvNnz8Jm2CqzokGzh2DBYr7MOGiP60eammw/Lt+H0r6Xfv\nMm/ZZ7wVPpilEbOzLF+2YA4fRSxlzpJVfPflapJu3+bgvt0cO3yAOUtW8fHCFVz76y8AVi6OpE//\nQcxdsgqAndt/sztfu6Y1zf0XGsH4BRuZPqSdZZmri5aZQzvQ9p0ltOy/kNCODSlRxBeAYW80I+r9\n7ni6Z/bfuL4hTFu+mZf7LcDD3ZXWwdUckO+f719bbcaFtWLasp95OXR+Rr7q9udrUt38/AhbzPhF\nPzN9ULbnx+A2tB2ygpYDlhHavj4lAnwt47/520uYsHQLE/q3NOd7qznTVm7l5fCleLi50rqRYnc+\ncGwfLhjXnZGz19GibwSJd3R0b1XPIRlFVjn5caMvgGcy/p2U0eaznG5AURStoiglFUXJybZypFGd\nCmzZYS5S7DpyjnrVS1uWVS33NKcvXOdWko50vYEdB+IJDqoIQPzFBHqMWJFlXUHVSvPH3lMAbN5x\njGb1q9ifr27FzHyHz1KveuYns6rln+b0hWuZ+fafJjioks02Pl4eTF28iTUbd9udKy/ymfvvJACb\ntx+lWQPHvOk2CizLlp0nzNs7eoF6VUtmZixXnNMXE7iVlGrOeOgcwXXKAzD9ndYs2xDHleu3Lff3\n8fJg6opfWfPTAYdkA8eOQR9vD6Yu/cmh+/jowf0816ARANVqBnLy+NEsy8tXrEzKnSTu3k0DkwmN\nBvbG7aBcxcpMHD2UD957lwaNmwAwftpsatWtR3p6OjcSruPj42t3vka1y7Nlx3Ego/+qWfVf+ac4\nfdG6/84QXNeq/95bmWVdB05cIqCQNwC+3h6k643258vF/rXV5oB6kQB/63wG+/PVLsuWOPPz7v7n\nR4msz4+DZwmuU46Y3/9k4IwNAJR5ujCJSebPdQdOXHF4PnBsH5YsUZidh84CEHvwDI3qVHBIRnuZ\nTKY8/3uccvImXVZV1fcBVFW9nfHvig9roCjKJxn/bQCcwPyzyUcURWloZ14A/Hw9SbyTWaQwGE24\nuJgfir+vJ7fv6CzLklJS8ff1BGDD1oP3DXaNRmN13zQK+XrZn8/Hk0SrDAaDMTOfT/Z8afj7edps\nc+5yAruPnLM7U17ly9J/yWkUyuhrh2RMTrOdMTlz/yelpOHv68l/X63LtVvJ/LLrVJZ1nbtyk93H\nLjoklyWfA8fgucs3HL6PU1KS8fH1s9zWurhg0GdWTcpWqMTAPj0Je70TDRo3wdfPn8Rbtzj551He\nn/oxg0aOZ8bEMZhMJlxcXPjrymXCXu/E7cRbVKhs/ydI8/617r9HjMF7/bft0H39d/r8NWYP78iB\nb0bxVBE/ft+bdf/nKl8u9q+tNqfPX2P2yE4c+G4sTxV1UD5vj6zbMljl8/Hg9h3r58ddS/8ZDEaW\nvd+FOcNeY+1m8wT59MXrzB7algNfDuWpIr78vv+M3fnAsX149lKCZVL9apOa+Hi5OySjyConEwKT\noiiWOrCiKFWB9Ee0KZ/x36lAa1VVGwAtgBm5SplN0p1U/Hw8LLe1Gg0Gg/lTwe07qfh6Z74p+Xl7\nkpiku28d9xiNJqv7epCYlGJ/vuRU/Lyt8mmt8iWn4utjnc+DxCTdQ9s4miPzGY2ZGf18PB7a1/88\nY+aT/r6MVlnuZXyzTT1efr4SP0eGElj5GT4Z35Wnitj/afaB+Rw4BvOCt7cPKSnJltsmo9Fy3D/+\n1Al27fiD1d9tYvV3P3Lr5g1+37oZ/0KFqNegEW5ubpQuWw43dw8Sb5qPSz/1zLOs/DqGNh26siTi\nY7vz3TeeNNn37/1j0JZZwzvQIiySOl1n8MWmPVkOP+Q6Xy72r602s0Z0okXfCOp0nsYXP+zOUjrP\ndb6UtIc8h9OyPT/cs7zJ9pvyLYHd5xA1uiPenm7MGtKWFuFLqdNzLl/8uD/L4Tm7MjqwD8MmrmFk\nn5ZsWjSQazeSSLiVObbzk9GU93+PU04mBCOALYqi7FEUZQ/wMzAsh+s3qKp6EkBV1cs53N4jxR48\nQ0hj83G4+jXLcuTUZcuy42evUqlMcQL8vXFzdaFxUEXiMkpND3JAvciL9SoB8Eqj6mzfH29/vgPx\nhASbv5hRv1a5rPnOZM9XibiDZx7axtEcme/A8Yu8WK8yAK80rsH2/acdk/HweUJeMH8SrV+jNEdO\n/5WZ8ew1KpUqSoCflzlj7XLEHblAy4HLeeWd5YS8+wmHTl4hdPI3/HXjjkPy3JfPgWMwL1QPrMvu\nWPMJqH8eOUS5ipUty3x8fPHw8MDdwxMXFxcKBxThzu3b1Khdlz1xOzCZTCRc+5tUnQ6/QoX58L1B\nXLpgrmB4eXtnqQrlVuzBs4Q0Nh/rr1+zLEdOX7EsO37mLyqVLpbZf3UrEHfYdgXl5u0UkjKqSVeu\n3SbAz9sB+f75/rXVxpzP/IZ85fptS3nernyHzhHygvnwpvn5cdUq399UKm31/KhTnrjD5+nZqg4j\n3ngJgJTUdIxGE0ajiZu3dZn9d/02AX72V0nBsX3YOrg6fd5fzavhCylayIdf41SHZBRZ5eS3DH5R\nFKUMUBtonfH3I/Cwj16FFEXZC/goihKK+TyE2YBD6qLR2w7RvIHCthVD0GggbOIaureqh4+XOyvW\nxzJqznpiFoSj0WpYHb2Ty9cSba5r9NwNRL3fA3c3F46f+Yt1v9p/nDl660GaN6zKtk+HodFoCPvw\nc7q3eg4fbw9WrNvOqNnriIkaiEaTme9BbfKKI/ONnrOeqA964u7myvH4q6z7Zb9jMv52jObPV2Lb\n4jDz9qZ+R/eWgfh4ebDi+92MivyRmLm9zRk37uWy1TkDj4Mjx2BeaPxSc/btjmVIWC8wmRg2bhJb\nN28iNSWFVzt04dUOXRj29pu4ubnxTMnStGzTHjc3N44c2Meg0Ncxmoy8M3wMLi4udHvjLT6e8gGu\nbq54engxZMyHdueL/t9hmjeowrZP3kWDhrBJa+keEoSPtzsr1u9k1LxoYiLN+351zK6H9t+AKV+z\neuob6A1G7qbrGTD1a/vz5WL/PqgNwIDJa1k97c2MfAYGTFlrf757z48l/a2eH7XN/Re9m1ERm4iZ\n18fcfz+Ynx/R/zvK0nFd2BLVDzdXF0bO30jqXT0DPlrH6kk9Mvtv+nq784Fj+/DU+WtsWjQQXWo6\nv+05yc/bnePaeM7wTQdH0jzqASmKUh7oD/QBCmM+DLBIVdVrj2jngXkSkYL5PIK3gE9UVX3U4QYA\nk1e9wTm4W/7Q7Z2PV9138juGTbr9C5w/X+Nx+R3DJt32qQA46xjU7Z0PwNmEfP2yz0OVK+qJ1/M5\nLSQ+frrdc5x2/0LGa0yjsfkdwybdjmmA0z9H7C9lPcIbXxzM8xnBZ6/XzvPHcc/DLkzUEXgbCALW\nA/8FlqmqOiknK1ZVNQ2w/sL3YjtyCiGEEE6lgBUIHnrI4DvgG+AFVVVPASiKkjdnuQkhhBAiXz1s\nQhAI9Ab+T1GUs8CXj7i/EEII8a9R0M4heNiVCo+oqjoCKAl8BDQFnlIUZaOiKI75XooQQgghnEJO\nvmVgAKKBaEVRigNvYJ4gbMrjbEIIIYTTetzXCchr/+gQQMY3C+Zk/AkhhBCigJBzAoQQQohc+Nec\nQyCEEEKIfw+pEAghhBC5ULDqA1IhEEIIIQRSIRBCCCFyxSjnEAghhBCioJEKgRBCCJELBaxAIBMC\nIYQQIjfka4dCCCGEKHCkQiCEEELkQgErEKBx0pKHU4YSQgjxxNDk9QY6fbI3z9+r1oXWy/PHcY9U\nCIQQQohcKGhfO3TaCYFXvcH5HcEm3d75eAUNyu8YNun2RTh//9V9J79j2KTbvwDAafexbl8EAF71\nR+RzEtt0uz7mdqoxv2PY5O+pdfox6KzjD6zGoJP24b3ncEGnKIoWiAJqA2lAX1VVTz3gfkuBG6qq\njn7Y+uSkQiGEECIXTKa8/3uEDoCnqqovAKOB2dnvoChKf6BWTh6PTAiEEEKIJ1Mw8BOAqqo7gees\nFyqK0ghoACzJycpkQiCEEELkgslkyvO/R/AHEq1uGxRFcQVQFOUZ4EMgx8d1nPYcAiGEEEI81G3A\nz+q2VlVVfca/uwLFgE3A04C3oijHVVX91NbKZEIghBBC5IIx/79ksB14DfhaUZSGwOF7C1RVjQAi\nABRF6Q1UfdhkAGRCIIQQQjyp1gMtFUXZgfm6C30URfkP4Kuq6tJ/ujKZEAghhBC5YMrna+ipqmoE\n3s72v48/4H6f5mR9clKhEEIIIaRCIIQQQuRGAbtQoVQIhBBCCCEVAiGEECJXnPTHAXNNKgRCCCGE\nkAqBEEIIkRtOcB0Ch5IKgRBCCCGezAqBRqNh/uiuBFZ5lrS7esInryX+4nXL8ldfrMHYfq3QGwys\n+j6OletjbbYJrFKSyLHd0BuMnDz3N+GT19p9XEij0TB/TFcCq5TM2NaXxF+wytekJmP7haA3GFkV\nvTMz30PadG9Vj/AeTWjae65d2Sz5nLj/LBnHds/sj0lf3N+HYa3NfbghlpXrd9hsU6F0MZZN3wuH\nswAAIABJREFUfAOTycTR01cY8tHXTrWPayulWDc/jFPnrwGw7Nv/49vN++3PN6oTgZWfIe2ugfCp\nXxN/MSEzX3B1xvZtmbGPd7MyOg5XFy1Lxnen7LMBeLi5Mn3FL2z845ilzcyh7Thx7hrL18XalQ3A\naDQyY+okTp44jpu7O+9/OJnSZcpalm+KieazVSvw9fWjbbsOtO/UxbLsRkICb/TswsIln1CufAXG\nvjeMhARz31+5fImatWozbeYcu/L9m8Zf8QBfFo7vSYC/Fy5aLaEffM4Zq9cDuzI6cR86gjNkcKTH\nUiFQFKWYoigaR62vXdNaeHq40rTPPMZHxjB9aAfLMldXLTOHd6TtwCha9osktGMjShTxs9lmXFgr\npi37mZdD5+Ph7krr4Or252tWC093N5r2npuxrY735xsQRcu+EYR2ysj3kDa1lVK82aEhGo1jutDZ\n+w+gXbNAPN1dafrmbMZHRDN9WKdsGTvTNnwBLUPnEdq5cUYfPrjNjOGdmbDwB1qEzkOj0fBa0xz9\nEugj8jluH9etVpqIz7cREhZJSFik3ZMBgHYv1TD3RegCxi/cyPTBr2Xmc9Eyc2g72r67lJb9FxHa\nsSElivjSs3U9biQm0yIsinaDlzF3pDlfscI+bJjXlzYvOmbfAvxv6y+k3U1jxWdreWfwMObNnmlZ\nduvmTRZHRbD4k1UsWbGanzb9wOVLlwDQp6fz0eQP8fTwsNx/2sw5LPlkNbPmRuLr58+wkQ/9yfcc\n+TeNv6mD2/PVj3to2TeCCVEbUcqVsDufOaNz96G4X55MCBRF6aMoygeKogQpinIc+AVQFUVp4Yj1\nN6pTgS07/gRg15Fz1Kte2rKsarmnOX3hOreSdKTrDew4EE9wUEWbbQ6oFwnw9wbA19uDdL3BAfkq\nZm7r8Nms+crbyvfgNkUKeTPxnbaM/Hid3bky8zl3/wE0qpu9P8pkZiz/NKcvXMvMuP80wUGVbLYJ\nqlaaP/aeBGDz9qM0a1DV/nwO3Md1q5Wm1Ys12LJ8EIs+6Imvt8f9G/zH+cqzJVY1b+vIeepVs873\nFKcvWuU7eIbguhVY9+tBJi75GTB/utMbjAD4eHswddlm1vy4z+5c9xzcv49GjYIBqBVYhz+PHrEs\nu3TxApWrVKVQocJotVqq16jJkUMHAJg3ZxaduvagWIn737SWRi2ge4/XKVbc/je0f9P4e6FOeUqW\nKMzGRQPp0fo5ft9zyu584Px96AgmU97/PU55VSEYAMwGZgHtVFWtAzQFPnLEyv18PUm8k2q5bTCa\ncHExPxR/X09u39FZliWlpOLv62mzzenz15g9shMHvhvLU0X9+H2v/U8GPx9PEq0yGAzGzHw+2fIl\np+Hv6/XANu5uriz+4D+MmrOepOQ0u3NZ8jl5/8E/7MOUNPz9PG22sa6sJCWnUcjX8/Hme8g+dnHR\nsufoOcbOi6Zl3wjOXEpgXFgrB+Wz3l/W+Ty4bbXMnM+TZN1d7qSk4evtwZqPejFx8U8AnLt8g91H\nz9udyVpy8h18/DJ/pE3r4oJeb/6RttJlyxJ/+hQJCddJ1enYvWsnOp2OmOj1BAQE8ELj4PvWdyMh\ngV1xsbRt3/G+Zbnxbxp/ZZ8pys2kFNqEL+TC1ZsM7+2Qz21O34eOYDSZ8vzvccqrCUG6qqrJQBIQ\nD6Cq6mVwzIWfk+6k4ueT+SlKq9FgyPg0c/tOKr7emYPFz9uTxCSdzTazRnSiRd8I6nSexhc/7M5S\nPs91vuRU/HwyM2i12sx8yalZPgH6+XiY8z2gTWCVZ6lYpjgRY7rx2fTeVC3/NLNGZJbdcp3PyfsP\nMvrQqp+0Wk3WPvSxzmjVhw9oYzQaM++b0d8OyeeAfWwwGPl+6yH2/3kBgO+3HqJ21VIOymdjHyen\nPSCfeYJQqkQhflr0Nmt+3MtXP9t/6MIWHx9fUpKTLbdNRiOuruZTmvz9CzF0xGhGDRvMuNEjUKpV\np3BAADEbviNu5w76h/bihHqcD8eN5vp183kXv/7yM61ebYuLi4tD8v2bxl9CYjIbfzP/SN6m348Q\nZFVtsDujE/ehuF9eTQi+VxQlGjgK/KAoylBFUX4Gtjpi5bEHzxDS2Hw8s37Nshw5ddmy7PjZq1Qq\nU5wAf2/cXF1oHFSRuENnbba5eTuFpGTzi+GV67ct5W+78h2Iz9xWrXJZ853Jnq8ScYfOPLDNnqPn\nqdf1I0LCInlj9KccP3PVIYcOnL3/IKMPg2uYt5eTPjx4xmabA8cv8mK9ygC80rgG2/efdkw+B+xj\ngJiF4TxXw1wabVa/imVyYFe+g2cJaWQuq9avWYYjp69a5fuLSqWLEeDvZc5XpwJxh89SoogvMZFh\nvL9gI6tjdtud4WFq1w1i+//9DsDhQweoWLmKZZler0c9foxln37OR7Pmcu5MPLXrBLF05ecsXfEZ\nSz5ZTRWlKhOnTqdYseIA7NoZS6PgFx2W7980/qxzBwdV5M/4qziCs/ehIxS0QwZ58i0DVVWnK4ry\nEhACnAdKABGqqm50xPqjtx2ieQOFbSuGoNFA2MQ1dG9VDx8vd1asj2XUnPXELAhHo9WwOnonl68l\nPrANwIDJa1k97U30BiN30w0MmLLWMfkaKmxbOdS8rQlfmPN5e7Bi3Q5GzdlAzMJwNFpt1nzZ2uQV\nZ+8/gOitB2nesCrbPh2GRqMh7MPP6d7quYw+3M6o2euIiRqIRmOV8QFtAEbPWU/UBz1xd3PlePxV\n1v1i/ydfR+7jQR99zZz3upCuN/BXwm0GTvnK/nz/O0LzBlXYtvwd87YmfUX3kLrmfbwhjlHzYoiJ\nCDP3X8wuLl+7zcfD2lPY34sxb7VkzFstAWg/ZBmpaXq782TXtHkL4mJ38FavnmAy8cGkafy06QdS\nUlLo1KUbAP/t3hkPD3de79WHwgEBD13fubNnKFnSMZ9s4d81/kbPXU/U+J6EdQkm8Y6O3mNX2Z0P\nnL8Pxf00Tvq1CZNXvcH5ncEm3d75eAUNyu8YNun2ReD0/Vf3nfyOYZNu/wIAp93Hun0RAHjVH5HP\nSWzT7fqY26nGR98xn/h7ap1+DDrr+AOrMeikfZjxHHbYN9tsaTZ/R56/gW4b3CjPH8c9cmEiIYQQ\nQjyZFyYSQggh8ptzFthzTyoEQgghhJAKgRBCCJEbTnoOXq5JhUAIIYQQUiEQQgghcqOAFQikQiCE\nEEIIqRAIIYQQuSLnEAghhBCiwJEKgRBCCJELUiEQQgghRIEjFQIhhBAiFwpYgUAqBEIIIYSQCoEQ\nQgiRK3IOgRBCCCEKHI2TznCcMpQQQognhiavN/DCjN/z/L0qdlSTPH8c98ghAyGEECIXnPQDda45\n7YTAK2hQfkewSbcvAq96g/M7hk26vfPxajAyv2PYpIub5fT7F5x3DFry1X0nn5PYptu/wOnzXbuj\nz+8YNhX3dXXa8QdWY7DhqHxO8mC6nTPyO8ITyWknBEIIIYQzK2AFAjmpUAghhBBSIRBCCCFypaCd\nQyAVAiGEEEJIhUAIIYTIjQJWIJAKgRBCCCGkQiCEEELkipxDIIQQQogCRyoEQgghRC4UsAKBVAiE\nEEIIIRUCIYQQIlfkHAIhhBBCFDhSIRBCCCFyoYAVCKRCIIQQQogntEKg0WiYP6YrgVVKknZXT/jk\nL4m/cN2y/NUmNRnbLwS9wciq6J2sXB/7yDbdW9UjvEcTmvae65h8o7sSWOXZjG2tJf6iVb4XazC2\nXyv0BgOrvo/LzPeANoFVShI5tht6g5GT5/4mfPJau49baTQa5r/XkcDKGdua9g3xFxMy8wVXY2xo\nS3P/xexiZfQuXF20LBnfjbLPBODh5sr0lb+y8Y9jVC1fgoVjuqBBw6kL1wmf9g0Gg9GufJaMDtrH\nxQN8WTi+JwH+XrhotYR+8DlnrPZHfudb/dGbPFXUH4CyzxZh1+Gz9Bqzyv58Y7tnbmvSF/fnC2tt\nzrchlpXrd9hsE1ilJHNGdcVgNJF2V0/f8av5+0aS0+S7Z+bwTpw49zfLv/0/u7IBGI1GZk+fzKkT\nKm7u7oweP5FSpctalv+08Xu+XL0SH19fXn2tA207dLYsO3r4EIsi57Bg6acAnDj+J+8NGUCpMub2\nHbt05+VXWtuVz9lfAy0ZR3YgsPIzpKXrCZ/23f2vM2+9bM74wx5WRu9Cq9UQNaYzVcoWx2Qy8e6M\n9RyL/4sKpYqybHxXTCY4Gn+VIbOineL4vTNkcKQ8qRAoiuKfF+u9p12zWni6u9G091zGR8YwfWhH\nyzJXVy0zh3ek7YAoWvaNILRTI0oU8Xtom9pKKd7s0BCNRuOYfE1r4enhStM+8zK21eH+fAOjaNkv\nktCOGflstBkX1oppy37m5dD5eLi70jq4uv35Xqph7ou+CxgftYnpg1/LzOeiZeaQdrQdtIyWby8i\ntENDShTxpWfrIG4kptCi/yLaDVnO3BHmfJPCW/NB1I80D1sIQBsH5APH7uOpg9vz1Y97aNk3gglR\nG1HKlXCqfL3GrCIkLJLuw5dzK0nHe7PXOyBfIJ7urjR9czbjI6KZPqxTtnydaRu+gJah8wjt3Dgj\n34PbfPxeF4bN+IaQfvOJ3nqA4X1aOlW+YgG+bFgQTpuXatmd654//vcrd9PSWPLpGt5+dygL5s6y\nLLt18ybLF0USuXQlC5atYvOPP3Dl8iUAvlj1CTMmf8DdtDTL/dU/j9L99TdZsPRTFiz91O7JADj/\nayBAu5eqm1/T+kUxfuFPTB/UJjOji5aZg9vSdvAntAxfQmj7+pQo4kub4GoANA9bxIQlm5nwdggA\nMwa3ZcKSzbR4ezEaNLzWxDGvMyKrvDpkcFVRlNA8WjeN6lRky44/Adh1+Cz1qpe2LKta/mlOX7jO\nrSQd6XoDOw7EExxU0WabIoW8mfhOW0Z+vM6B+SpkbuvIuaz5ytnK9+A2B9SLBPh7A+Dr7UG63mB/\nvtrl2bLzeMa2zlOvaqnMfOWf4vTFhMx8B88QXKcC6349xMQlPwOgQYM+owrQY/Rqth84g5urC08V\n9SPxjs7ufODYffxCnfKULFGYjYsG0qP1c/y+55RT5btn/Nuvsmjt71y9ftv+fHWzb6tMtnzXMvPt\nP01wUCWbbXqNXsmhE+Y3PFcXF1LT0p0qn4+XB1MXb2LNxt1257rn0IF9NGgUDEDNWrU5fuyoZdnl\nSxeoVEXBv1BhtFotVWvU5OjhgwCULFWaqR/Pz7Iu9c9jxP7fbwzs24uPJo0nJTnZ7nzO/hoIGa8z\nsSfM2zua/XWmRLbXmbME1ylPzO/HGDjdnKPM04VJTEoFIEgpyR/74gHYHKvS7PlKDs2aWyZT3v89\nTnk1ITgI1FUUZauiKC85euV+Pp5Z3ngMBiMuLuaH4u/jyW2rZUnJafj7ej2wjbubK4s/+A+j5qwn\nKTlzRm93Pl9PEu+kZm7LaMrM55stX0oq/r6eNtucPn+N2SM7ceC7sTxV1I/f99r/Zubn45FtW9b9\n55EtXxr+vp4k6+5yJyUNX28P1kx/g4mLfwLAaDRR5unC7Fs7nKKFvTl88ord+cwZHbOPXVy0lH2m\nKDeTUmgTvpALV28yvHcLp8oHUDzAl6b1q/BZTJzd2f5xvpQ0/P08bba5N0FpWLs8b3dvQuQX25wq\n37nLCew+cs7uTNaS7yTj4+tnua3VatHr9QCUKlOWM6dPcSPhOqk6HXt3xZGqM+dq+vIruLpmPRJb\nrWYtBgwZwcLlq3m2ZClWLI2yO5+zvwaaM3qQmGzjddDHk9tWy+69ztzLtWx8N+YMb8/an/cDZKlc\nJKWkUSjjvsKx8mpCoFNV9R3gPWCQoiiHFUWZpyjKIEesPCk5FT+fzAGh1Wotx61vJ6fi6+1hWebn\n40Fiku6BbQKrPEvFMsWJGNONz6b3pmr5p5k1IrN0met8d1Lx88nMoNVoMvPdScXXOzOHn7enOZ+N\nNrNGdKJF3wjqdJ7GFz/sznL4Idf5ktPws+ojrdYqX3IavlY5/Lw9LC8ipUoU4qeo/qz5cR9fbT5g\nuc/5q7eo1WUmy9ftZMaQzMMP9mV0zD42GIwkJCaz8bfDAGz6/QhB2T6Z53c+gI4t6vDVT3sxGh3z\nkSApOfUh+zgVXx/rMWiVz0abLq8EETG2Bx0HLeL6zTtOl8/RfHx9snySN5lMljd6f/9CvDt8FONG\nDmHCuJFUqVqNQoUDbK6rSbOXqVqthuXfJ9U/7c7n7K+B5owPe53JltHbw1INAOg3+WsCu84iakxn\nvD3dMFp9VM5+3/xkMpny/O9xyqsJgQZAVdU9qqp2BoKBXwF3R6w89kA8IY3Nx5Dq1yrHkVOXLcuO\nn7lKpTLFCfD3xs3VhcZBlYg7dOaBbfYcPU+9rh8REhbJG6M/5fiZqw4pm8UePJO5rZpls+Y7mz1f\nReIOnbXZ5ubtFJIyZtJXrt+2HD6wK9+hs4Q0qpaxrTIcOXU1M9+Zv6hUuhgB/l7mfHUrEHf4HCWK\n+BIT0Y/3F2xidUxmafabWb2pWLoYAHdS0rI8ce3K6KB9bFlXsPkFOTioIn/GX8VejswH0LyBwubt\nx+zOlSVfxmPOUb6DZ2y26fHq87zdvQkh/eZz9lLC/RvL53x5oVbtuuzc/jsARw4fpEKlypZler2e\nE8f/JOqTz5g0fQ7nz56hVu26Ntc1bGAYx44cAmDvrjiUavYf/3b210C49zqjmLdXowxHTlu/zvyd\n7XWmPHFHztGzVV1G9GoKQEpqOkaTCaPJxIETl3gxqAIAr7ygsP3gGYdktFdBmxDk1bcMPrW+oapq\nIhCT8We36G2HaN5QYdvKoWg0EDbhC7q3qoePtwcr1u1g1JwNxCwMR6PVsjp6J5evJT6wTV6J3naI\n5g0Utq0YYt7WxDXmfF7urFgfy6g564lZEI5Gq8maL1sbgAGT17J62pvoDUbuphsYMGWt/fn+d4Tm\n9SuzbdlANBoNYZO/ovsrdcz9tyGOUfNiiJnfz5wvZjeXr93m42HtKOzvzZi3WjDmLXPJvf3Q5cxe\nvY1l47tzV68nJTWdAVO/sTsfOHYfj567nqjxPQnrEkziHR29x9p3Br+j8wFULluCMxcd82YLEL31\nIM0bVmXbp8PM+/jDz+ne6rmMfNsZNXsdMVHm/W/J94A2Wq2G2e914cLVm6yd3Q+AP/aeZMriTU6R\nL680adaC3XGxvN3ndUwmE2M/nMLmH39Ap0uhfaduALz1ehfc3T3o8d83KRxgu0IwYswHzJs1FRdX\nN4oWLcZ74ybYnc/ZXwMBov93lObPV2bb0gHm7U35xvw64+XOiuhdjJr/AzHzQjNeZ/Zw+dptov93\nhKXvd2PLov64ubowcm4MqWl6Rs/fSNSYzri7uXD87N+s23o4T7P/W2mc9GsTJq8ghxxdyBO6fRF4\n1Ruc3zFs0u2dj1eDkfkdwyZd3Cycff8CTpvRkq/uO/mcxDbd/gVOn+/aHX1+x7CpuK+r044/sBqD\nDUflc5IH0+2cARmV6rxU8/0tef4GemRKyzx/HPfIhYmEEEII8WRemEgIIYTIb05aYc81qRAIIYQQ\nQioEQgghRG4UsAKBVAiEEEIIIRUCIYQQIlccdSExZyEVAiGEEEJIhUAIIYTIDTmHQAghhBAFjlQI\nhBBCiFyQ6xAIIYQQosCRCoEQQgiRCwWsQCAVAiGEEEJIhUAIIYTIFTmHQAghhBAFjsZJZzhOGUoI\nIcQTQ5PXG6g4/Mc8f686Pbt1nj+Oe6RCIIQQQgjnPYfA67mh+R3BJt2euc6fL2hQfsewSbcvAq/6\nI/I7hk26XR8DzjsGdXvmAjh9H3rVG5zfMWzS7Z3v9Pku3ryb3zFsKhXgDuC0fajbO/+xbMdJK+y5\n5rQTAiGEEMKZFbQJgRwyEEIIIYRUCIQQQohcKVgFAqkQCCGEEEIqBEIIIUSuyDkEQgghhChwpEIg\nhBBC5IJUCIQQQghR4EiFQAghhMgFqRAIIYQQosCRCoEQQgiRC1IhEEIIIUSBIxUCIYQQIjcKVoFA\nKgRCCCGEeEIrBBqNhvmjuxBY+VnS0vWET/6K+IvXLctffbEGY/u+gt5gZNX3cazcsNOy7PkaZZgy\n6DVC+i8EILDKs8wZ2RmD0UjaXT19P/yCv2/ccZp8Vcs/xcJx3dBoNJw6f43wKV9hMBjtzzemK4FV\nSpJ2V0/45C+Jv2CVr0lNxvYLMeeL3snK9bE22xQP8GXh+J4E+HvhotUS+sHnnLF6rHZlHNWJwMrP\nkHbXQPjUr4m/mJCZMbg6Y/u2RG8wsOr73ayMjsPVRcuS8d0p+2wAHm6uTF/xCxv/OEZg5WeZM7ID\nBoOJtHQ9fSd86VT7OM/GoIP6756ZQ9tx4tw1lq+LtSubJd/orgRWeTZjPK29v//6tcrIF8fK9Znb\nfL5mWaa8+xoh/RcAUKFUMZZNfB2TycTR01cYMv1bu4/tOjLfPTOHdeTEub9Z/t12u7IBGI1G5s+a\nwumTKu5u7gwfO5GSpctYlv/y0w98s2Y1Li5aWrXtSLvO3dHr05kxaRxXr1xGq3Vh+JgPKVOuAqdO\nHCdy9ke4aLW4ubsz6oOpFClazO6Mzr6PHcEZMjjSE1khaNe0Jp7urjR9az7jI39g+tB2lmWuLlpm\nDmtP23cW0zJsAaEdX6BEEV8AhvVqTtT47ni6Z86DPh7ekWGzviOk/0Kitx1i+JsvO1W+SQPb8MHC\njTQPjQCgzYs17M/XrBae7m407T2X8ZExTB/aMTOfq5aZwzvSdkAULftGENqpESWK+NlsM3Vwe776\ncQ8t+0YwIWojSrkSducDaPdSDXMfhi5g/MKNTB/8WmZGFy0zh7aj7btLadl/EaEdG1KiiC89W9fj\nRmIyLcKiaDd4GXNHmjN+PLw9w2ZtICR8EdHbDjO8VzP78zn7GHRg/xUr7MOGeX1p82J1u3NZ8jWt\nhaeHK037zMsYTx0y890bgwOjaNkvktCO5jEI9/qvB54ebpb7zxjWgQlRG2nRNwINGl5rWsup8hUr\n7MOGiP60eamm3bnu2f7bVu6mpbFg+Rf0HTiExRGzsixfEjmbWZHLmL/0M775chVJtxOJ2/EHBoOB\nyGWf88Zb/VmxOBKAhXOn8+7wMcxZtJLgpi+z9rMVDsno7PtY3O+xTAgURXFXFMXLUetrVKcCW2KP\nA7DryDnqVSttWVa1/FOcvnCdW0k60vUGdhw8Q3DdigDEX7xOj5Ers6yr19jPOHTiMmB+oUxNS3eq\nfD3eW8n2/fG4ubrwVFE/Eu/oHJCvIlt2/GnOd/gs9apb53s6a74D8QQHVbTZ5oU65SlZojAbFw2k\nR+vn+H3PKbvzmTOWZ0usat7ekfP39+HF7H1YgXW/HmTikp8B86cTfUYlpde4zzl00nof6x2Qz9nH\noOP6z8fbg6nLNrPmx31258rMVyFzPB05l3UMlnvwGASIv5hAjxFZ37CCqpXmj73mcbd5xzGa1a/i\nVPl8vD2YuvQn1mzcbXeuew4f3MfzLwQDUL1mbdTjx7Isr1CpCsnJSdy9m4bJZEKj0VCqdDkMegNG\no5GU5GRcXM2T0vcnz6JSlaoAGAwG3D08HJLR2fexI5hMpjz/e5zyZEKgKEoVRVG+VRRljaIoDYEj\nwFFFUbo7Yv1+Pp5Z3hgNRhMuLuaH4u/jye07qZZlScmp+Pt6ArBh6yHS9YYs67qacBuAhoHleLvb\ni0Su+c2p8hmNJso8HcC+r0dRtLAvhzPe2Byaz2DMli9zWVJyGv6+XjbblH2mKDeTUmgTvpALV28y\nvHcLu/NlZszsJ4PROqNHtj5Mw9/Xk2TdXe6kpOHr7cGaj3oxcfFPAFxNSAKgYa2yvN21MZFf/u6g\nfM4+Bh3Tf+cu32D30fN2Z8qSzzd7Pqv+8802BlOs++/gff2n0Wis7ptGIV/7P3s4Mt+5yzfYfeSc\n3ZmspSQn4+Pja7ntotVi0GdOdMtVqER47+6E9uxAw8Yv4evnj5e3N1evXKZ393bMmT6BTt1eB6Bo\nseIAHD10gOhvvqRLjzccktHZ97G4X15VCJYBi4HvgB+AZkAtYIgjVp6UnIqft6fltlajsRxXv52c\niq9P5gw3+wvjg3RpWYeIMV3pOGQZ128lO12+81dvUqvTNJZ/t50ZVmU3u/L5WOXTarPm87bO50Fi\nks5mm4TEZDb+dhiATb8fIcjqU4D9GTNzZO3DtAdkNPdhqRKF+GnR26z5cS9f/bzfcp8uLWoTMboz\nHYd+4pT7OE/GoAP7z9GS7jwk351UfK361s/bk8Qk25Uxo9FkdV8PEpNSnCpfXvD28UGXkjlOjEaj\n5RP/6ZMqcTt+5/N1P/HF+p+5dfMGv/36M99+uZrnGzZi9Tc/sPSz75gxaRx309IA2LblJ+bOmMTU\nOQspHFDEIRmdfR87glQIcsZVVdVfgHVAgqqql1RVTQbsr4UCsQfPENK4GgD1a5blyKkrlmXHz/xF\npdLFCfD3xs3VhcZ1KxB36KzNdfVoXY+3u71ISP+FnL2UYPN++ZXvmzmhVCxtPsHnTkoaRqN9JxQC\nxB6IJ6Sx+Xhw/VrlOHIqs+pw/MxVKpWxyhdUibhDZ2y2iT0QT0iw+byG4KCK/Bl/1e58ALEHzxLS\nyFzGrF+zDEdOZ67X3IfFCPD3MmesU4G4w2cpUcSXmMgw3l+wkdUxmeXZHq2CeLtbY0LCF3H28g0H\n5XP2Mei4/ssL5v7LGE81y2Ydg2ezj8GKD+2/A+pFXqxXCYBXGlVn+/54p8qXF2oG1iVuxx8AHDty\nkPIVK1uW+fr64eHhiYeHJy4uLhQOKEJS0m38/P3x8TVXFfz8/dHr9RiMBrb8GMOGb79kTtRKni3p\nmAk9OP8+LggURdEqirJYUZRYRVH+pyhKpWzLeyqKEqcoyvaM+z30PT+vvmVwVlGUtRnrv6MoylQg\nEbjy8GY5E73tMM0bKGz7ZBAajYawiV/SPSQIH28PVqyPZdTcaGIi+6PRalj9fRyXryXgVWgmAAAb\nrklEQVQ+cD1arYbZIzpy4eot1s7qA8Afe08zZelPTpEPYPanv7Jswn+4m64nJTWdAZO/siubOd8h\nmjdU2LZyKBoNhE34gu6t6pnzrdvBqDkbiFkYjkarZXX0Ti5fS3xgG4DRc9cTNb4nYV2CSbyjo/fY\nVXbnA4j+3xGaN6jCtuXvmLc36Su6h9TFx8udFRviGDUvhpiIMDQaDatjdnH52m0+Htaewv5ejHmr\nJWPeaglAx6GfMHt4By78dZO1M3oD8Me+00xZttm+fM4+Bh3Uf+2HLHPIORf35dt2yNx/K4aY801c\nYx6DXu7m/puznpgF4eb+yxiDtoyeu4Go93vg7ubC8TN/se7XA06VLy8EN32ZvbtjebfffzGZTLz3\n/mR+/XkjOl0KbTt0pW2Hrgzu3ws3VzeeKVWakDYd0KenM2vqeAb3fxN9ejqh4YNwd/dg4dzplHjq\nGSaMNhdwA4Oeo3e/gXZndPZ97BD5/yWDDoCnqqovZByenw20B8g4b28KUEtV1RRFUb4E2gLf21qZ\nJi9KEoqiuAKvAieAO8BQ4AYwL6NS8Cgmr+eGOjyXo+j2zMXp8wUNyu8YNun2ReBVf0R+x7BJt+tj\nAKfdx7o9cwGcvg+96g3O7xg26fbOd/p8F2/eze8YNpUKcAdw2j7U7Z0PoHnU/ez1bP91eT4luLyk\nk83HoSjKHGCXqqprM25fUlW1ZMa/tUBxVVX/yrj9DbBMVVWbn4bypEKgqqqerLOQ4XmxHSGEECK/\nOMF1CPwxV9/vMSiK4qqqql5VVSNwbzLwLuALbHnYyp7ICxMJIYQQ+c0JJgS3AT+r29qMD+SApUow\nE6gCdFZV9aGBn8gLEwkhhBCC7ZgPz5NxDsHhbMuXAJ5AB1VVH/nVDKkQCCGEELngBBWC9UBLRVF2\nYD5noo+iKP/BfHhgDxAK/AFsVRQFYL6qquttrUwmBEIIIcQTKOM8gbez/e/jVv/+R0cBZEIghBBC\n5IITVAgcSs4hEEIIIYRUCIQQQohcKVgFAqkQCCGEEEIqBEIIIUSuyDkEQgghhChwpEIghBBC5IJU\nCIQQQghR4EiFQAghhMgFqRAIIYQQosCRCoEQQgiRGwWrQIDGSUseThlKCCHEE0OT1xso2uvLPH+v\nSljdM88fxz1OWyHwqjc4vyPYpNs7H6+gQfkdwybdvgi86r6T3zFs0u1fgNdzQ/M7hk26PXMB8Go4\nKp+TPJhu5wzgCXiOOPsYbDAyv2PYpIub9UQ8Ry7fupvPSR7s2cLuj2U7TvqBOtfkHAIhhBBCOG+F\nQAghhHBmUiEQQgghRIEjFQIhhBAiFwpahUAmBEIIIUQuFLQJgRwyEEIIIYRUCIQQQohcKVgFAqkQ\nCCGEEEIqBEIIIUSuyDkEQgghhChwpEIghBBC5IJUCIQQQghR4EiFQAghhMgFqRAIIYQQosB5IisE\nGo2G+aO7EljlWdLu6gmfvJb4i9cty199sQZj+7VCbzCw6vs4Vq6PtdkmsEpJIsd2Q28wcvLc34RP\nXmv3rE+j0TB/TFcCq5TM2NaXxF+wytekJmP7haA3GFkVvTMz3wParP7oTZ4q6g9A2WeLsOvwWXqN\nWWV/vrHdM7c16Yv784W1NufbEMvK9TtstgmsUpI5o7piMJpIu6un7/jV/H0jya58loyjuxBY+VnS\n0vWET/7q/n3c9xVzxu/jWLlhp2XZ8zXKMGXQa4T0XwhAbaUk6+b25VTGY1z27Xa+3XLA/nwjOxBY\n+RlzvmnfEX8xITNfcDXGvvWyOd8Pe1gZvQutVkPUmM5UKVsck8nEuzPWcyz+LyqUKsqy8V0xmeBo\n/FWGzIp2zBh00HOkTtVSRI7pRlq6nkPqJYZ/vM4x+Rw0BiuULsayiW9gMpk4evoKQz762jH53uto\nHn939YRP++b+/Rva0pwvZhcro3fh6qJlyfhulH0mAA83V6av/JWNfxxj9ZTXeaqIHwBlnwlg19Hz\n9Hr/C/vzOej5UbX8Uywc1w2NRsOp89cIn/IVBoPRrnwARqOReTOncPqkipu7OyPHTqRk6TKW5Vt+\n+oFv1qxGq9XS+rWOtO/cnbt37zJj8vtcuXQJHx8fBo8cR6kyZbl04TzTJ72PRqOhfMVKDB45Dq02\n/z/PSoXACbRrWgtPD1ea9pnH+MgYpg/tYFnm6qpl5vCOtB0YRct+kYR2bESJIn4224wLa8W0ZT/z\ncuh8PNxdaR1c3f58zWrh6e5G095zM7bV8f58A6Jo2TeC0E4Z+Wy06TVmFSFhkXQfvpxbSTrem73e\nAfkC8XR3pembsxkfEc30YZ2y5etM2/AF/H97dx4fVXX+cfwzCZCQBUUrWhWV9RFQgQCKgIAUDCKo\naC3anzuyKIJLRdzwB2KtVhFBCJuCG9SlRTEugAsKImBFVpWHNVqlKKKSkAVIMv3j3CSTkETITJzJ\n+Lxfr7ySmTv33m/uMnPuOWfu6TXwCQZe2tnLV/48j935R25/5BVSB01k/vtr+Mt1vYLOB3Bh99Pc\n+q6fyOgn3+Dh2y4syRgbw99vv4i+N0+j1+DJDOx/Ng2OSgLg9qt7kDZ6APF1Ssq6bU89kUlzPiR1\nyBRSh0wJujAAcGG3lu54GpTG6CkLeHjEBaXz3dKXvrc8Ta8bpzPwojNpcFQSF3RpAUCPwVMZM30R\nY4amAvDILX0ZM30RPYdOw4ePfl1DcAyG8ByZfO8ARo6fR88bJrFnby4DercLPl8Ij8FH/nIpY6a8\nQc+BT+Dz+ejX/fTg83Vr5c7HGyYzOu0tHr6lX0m+2Bj+fuuF9B0xk15DpzLw4o40OCqJK85P4cc9\nOfQcMpULb32KCXe47Xf1fXNIvWkaA0Y9y89787hzwuvB5wvh+fHAsAu4f8qb9Bg4CYALzmkVdD6A\njz58n/379zHl6TkMvulW0iY+Wmr6tEnjeezJmTw583lenvssWZl7eHP+P6lbN4G0WXMYfsfdTHzs\nIQDSJj7KwKHDmTTjWfx+P8uWLA5JRlNatRcIRMQX6mV2atOYdz7+EoBPNnxFu5YNi6edespxbP3P\nD/yclcuB/AI+XrONLilNKpxnjX5D/XoJACQlxHEgvyAE+ZqUrGt9Rul8jSrKV/E8AKOH9mHqi0vY\n+UNm8Pnall1XSand5dtVkm/1VrqkNK1wnqvvms26Td8CUCs2lrx9B4LOB94+Xr7RrW/DV7RrEbgN\njy29Dddup0vbJgBs++YHLh85u9Sy2rZoSO8uLXlnxs1MHT2ApIS44PO1bsQ7yze5fJ9/TbtTTwzI\n14Ct3+wOyJdBlzaNSF/yBcMengfASccdyZ6sPABS5ASWfrYNgEXLlXM7NA0+XwjPkRMaHMmKdRkA\nLF+7nU5tGgefL4THYEqLhixdtRmARcs+59yzTg0+X+tGvLOi6Pgru3+PLbN/t9OlTWPmvbeOsdMX\nAuDDR36Zq+zRg85j6ssfsXN38DVooTw/Lr9zNstWb6N2rViOPTqZPXtzg84HsH7tZ5zZsQsALU9v\nzaaNX5Sa3rhpc7Kzs9i/bx/4/fh8PjK2b+OsTucAcNLJjfg6w50XmzZ+QeuU9gCceXYXVn2yPCQZ\ng+b/FX5+RdVSIBCRJiKyQES+AvaLyAoRmSsix4Vi+clJ8ezZm1f8uKDQT2ys+1fqJcWTGXBAZ+Xk\nUS8pvsJ5tn69i/EjL2HNv+7h2KOTWbJqS/D5EuNLnVQFBYUl+RLL5MveR72kupXOc0z9JLqf2Zzn\n01cGne2w8+Xso15yfIXzFBVQOrZuxNABXXlyTmhK7getL3AfJ8aTGbAvs7LdPgZ47f11BxXqPv38\na+6Z+Dq9Bk9m+7e7uXdQagjyxbEnu4JjMDGezIBpWTn7ivMVFBQyc/SfePwvF/HiwtWAq/4NfO0R\n3muDyhfCcyTj2910SXEfKH26nkZi3TrB5wvhMVhq+2WHaPslxpXZFoH54g7OlxRPdu5+9ubsIykh\njrkPX8XYaQuKX3NM/US6d2jK829+GnQ2ly9050dhoZ+TjqvPZy+P4ugjk1i/eUdIMuZkZ5OYlFT8\nOCYmhoL8/OLHjZo0Zcg1A7juiovp2LkbScn1aNpMWP7Rh/j9fr5Yv5Yfdn1PQUEBfq/AAJCQmEh2\n9t6QZDSlVVcNwRRghKqeDJwDLAbGA0+HYuFZe/NITiy5yovx+YrbvDL35pGUUPKGkJwQz56s3Arn\nefSOS+h5wyTaXPoQc974d6mq1Srny84jObEkQ0xMTEm+7LxSV6jJiXEuXyXz9O/ZhpcWrKKwMDTF\nxazsPJIDMsTE+ErnSwzcfgH5Kpjnj+elMOmey+k/Yio//BSaE9WtL2B7+MpmDNyGpT/Iynp98TpW\nb/zG+3s9reWEEOTbV/k2DNzHCXHFtQEAg8a9zBmXPUra3ZeSEF+bwoB2yLKvrXK+EJ4jg8fOZeR1\nvXhr6jB2/ZjF7p+zg88XwmOwsLDkSrzofAo+X2X7d1/p4y8hrvjD+cQGR7AgbQhz3/6MlxaVNE31\n73EGLy1cHeJzODTnB8DXO3/i9Ese4ql/LeORELwHgvvgzskpOVYKCwuJreWaKrZuVlYsW8LcVxfw\nj9cW8vNPP/LBewvp068/iYmJjBh8DUs/fI/mp7YkNja2VKEvJzubpKTkkGQMlt/vr/afX1N1FQiO\nUNVNAKq6AuisqquA+qFY+PK120nt7NpZzzztZDZsKSnRbszYSdOTjqF+vQRq14qlc0oTVq7LqHCe\nnzJzyPKu5v77Q2Zx80FQ+dZsK1nX6aeUzre9bL6mrFy3vdJ5epwlLFpWurot6HxdWh16vrXbK5zn\n8j4dGDqgK6mDJpLx7e6DV1bVjGu3k9rZtbm7/fXfgIzf0bRhQMa2jVnpVWmXJ33yUNq3ctXL557Z\nvLhwEFS+dRmkdhKXr9VJbNi6MyDf9zRt+Dvq16vr5WvEyg1fcUXvttxxdXcAcvIOUOj3U+j3s2bT\nt5yT4qrhzztbWLZ2e/D5QniOnN+lJdfd9xx9bpzC0Uck8t5KDT5fCI/BNRu/4Zx2zQA4r3Mrlq3e\nGny+dRmkdio6/k5iw5bA/ftdmf3bmJXrv6LBUUmkTxrEfZPf4rn0f5daXo8OzVi0PPjtVpwvhOfH\nK48PpEnD3wGwN2dfqQJWME47oy0rP14KwBfr19K4abPiaYlJycTFxRMXF09sbCxH1j+KrMxMNn65\ngZQOHXly5nN0/0Mqvz/eNdU0kxasWeW26SfLP+L0NsH3YzEHq65vGWwTkWnA20Bf4FMRuQAI/tIC\nmL94HT3OEhbPuhWfDwaPncuA3u1IrFuHWa8uZ9Tjr5I++UZ8MT6em7+CHbv2lDsPwE3jXuS5h64h\nv6CQ/QcKuOnBF0OTr6OwePZtbl1j5rh8CXHMmvcxox5/jfQpN+KLiSmdr8w8RZqd3IDt34Tuw3b+\n+2vp0fFUFj9zOz6fj8H//wIDerf38i1j1Ph5pKcNw+cL2H7lzBMT42P8nX/kPzt/4sXxgwBYumoz\nD057K/iMi9e7/fX0CLe+sf9gQGqKy/jqckZNmE/6k0PcPn59JTt27alwWSP+9k8ev/MSDuQX8N3u\nTIb99eXg833wOT06NGPxjJvc/nrwFQac18Ydg/M/YdTEN0h/YqDLl/4pO3ZlMv+DDcy470+8M3UI\ntWvFMnJCOnn78rlr4puk3X0pdWrHsjHje+a9vz74fCE8R7Z8vYu3pg4jN+8AH366mYUhKJyG6hgE\nuOvxV0m7/wrq1K7Fxm07mffu6uDzfbCBHmc2Y/FMl2HwuJfc/k2IY9ZrKxn1RDrpEwd5+/ff7NiV\nyWO3X8iR9RK4+/qe3H19TwAuuu0p8vbl0+zkY9gewgJzKM+P8c+8x8wxf2b/gXxy8g5w07iXQpLx\nnO5/YNUny7n5hivx+/2MGj2Odxe+SW5ODv36X0a//pcxYvDV1KpVm+NPbEjvvheTk72XB6aP5IXZ\nM0hKTmbkvQ8AcOMtd/DYQ2PITzvASac0pluP0HReDla0fcvAVx3/kIjUAQYBLYE1wCygA7BZVQ/l\nrPDXbXdLyHOFSu6qidRNGRHuGBXK/WwSddveHO4YFcpdPZm67W8Ld4wK5X46AYC6HUeFOUn5clc8\nAkDEnyORfgyeNTLcMSqUu/LRGnGO7Ph5f5iTlO/4I+sAhLxDe1l1L3m62ksEufMGVvv/UaRaaghU\ndT+uH0GgFeW91hhjjKmRoqyGoEbeh8AYY4wxoVUj71RojDHGhJ0/NB0wI4UVCIwxxpiqsCYDY4wx\nxkQbqyEwxhhjqiLKmgyshsAYY4wxVkNgjDHGVIn1ITDGGGNMtLEaAmOMMaYqrA+BMcYYY6KN1RAY\nY4wxVWE1BMYYY4yJNlZDYIwxxlSFfcvAGGOMMdHGagiMMcaYqoiyPgQ+f2RWeURkKGOMMTWGr7pX\nUPf8CdX+WZX79m3V/n8UidQagl9tAxhjjDFVEpkX1FVmfQiMMcYYE7E1BMYYY0xki7I+BFZDYIwx\nxhirITDGGGOqxPoQGGOMMSbaWA2BMcYYUxVR1ocgqgsEIhIDpAGtgX3ADaq6JbypDiYiZwGPqGr3\ncGcJJCK1gVnAKUAc8KCqvh7WUAFEJBaYCQju3hVDVXVDeFMdTEQaAKuAXqq6Mdx5yhKRz4BM7+F2\nVb0unHnKEpG7gQuBOkCaqj4d5kjFRORa4FrvYTzQBjhOVX8OV6ZA3jn8LO4cLgAGRdIxKCJxwGyg\nMe4YHKaqm8Ob6jBYk0GNcjEQr6pnA3cB48Oc5yAicifwFO7NJNJcCexW1XOA3sDkMOcpqx+AqnYG\n7gP+Gt44B/PekKcDueHOUh4RiQd8qtrd+4m0wkB3oBPQGegGNAxroDJU9ZmibYcr9I2IlMKApw9Q\nS1U7AQ8QeefIIGCvqnYEhhN57zG/KdFeIOgCLABQ1RVA+/DGKddW4JJwh6jAK8Bo728fkB/GLAdR\n1deAwd7Dk4FIeiMu8hgwDdgR7iAVaA0kiMgiEXlfRDqGO1AZqcB64FUgHXgjvHHKJyLtgVaqOiPc\nWcrYBNTyakvrAQfCnKeslsDbAKqqQIvwxjlM/sLq//kVRXuBoB6wJ+BxgYhEVDOJqv6LyDtJAVDV\nvaqaJSLJwD9xV+ERRVXzReRZ4ElgTrjzBPKqk3ep6sJwZ6lEDq7QkgoMBeZE2DnyO1xB/jJK8kXi\nnUzvAcaGO0Q59uKaCzbimtcmhTXNwdYAfUXE5xVGT/CaAk0YRHuBIBNIDngco6oRdZUb6USkIbAY\neF5V54Y7T3lU9RqgOTBTRBLDnSfA9UAvEfkA17b8nIgcF95IB9kEvKCqflXdBOwGfh/mTIF2AwtV\ndb93BZkHHBPmTKWIyJGAqOricGcpx2247dccVxv0rNdMFClm4d6nlwL9gVWqWhDeSIfB76/+n19R\ntBcIluHa0PBKn+vDG6dmEZFjgUXAKFWdFe48ZYnIVV6HM3BXuoXeT0RQ1a6q2s1rX14DXK2qO8Mc\nq6zr8frWiMjxuFq1/4Y1UWkfAb29K8jjgURcISGSdAXeC3eICvxESS3pj0BtIJKuwDsA76lqF1wT\n5bYw5/lNi6SqwerwKu4K7WNcG3hEdZiqAe4B6gOjRaSoL8H5qhopHeTmAbNFZAnuje7WCMpWUzwN\nPCMiH+G+qXF9JNWiqeobItIV+AR3ATMsAq8ghcj9IJsAzBKRpbhvadyjqtlhzhRoMzBORO7F9QEa\nGOY8hyfKvnYYqcMfG2OMMRGt7rnjqn/448Wjf/PDHxtjjDGRrTC6LqijvQ+BMcYYYw6B1RAYY4wx\nVRFlfQishsAYY4wxVkNgjDHGVEmU1RBYgcCYXyAip+Bu4PMF7qt5dXC3Ir5OVb+p4jKvBbqr6rUi\n8hZu4K1yb28sImOBd1V16WEs36+qvjLPjQFQ1TGVzJfh5co4xPX84jKNMTWDFQiMOTQ7VLVN0QMR\n+Rvudsn9g12wqvb5hZd0w90t0hgTSaLsa/tWIDCmapbghuQtuqpeibs9cdHIkLfi+uiswt1MJ09E\nrsKNB5EJfIW7z3zxVTmwE5iCG5TrADAON+x0e+ApEemPGzVxKnA07u6Mw1V1tVeL8QKQBKz4pfAi\ncjNwFe7Of4XAAFX90ps8RkRa424TPERV13l3rZyOG22wELhbVd89rC1mjIlo1qnQmMPkDWk8AHdr\n7CJvq6rg7rM/COjk1Sh8D9zh3Xb377jb3J5N6TE2igzHfaC3AHoC9wMvAp/imhTW48a2v1NVU3Aj\nPb7ozTsZeMZb57KyCy6Tvx5uaPDuqnoa8BpwU8BLNqtqW1yB5FnvuYnALFVthysITfcGvTLmtyvK\nRju0GgJjDs3xIrLG+zsOdyvduwKmr/R+nws0A1aICLj+Bp8BnYCPVfU7ABF5AfhDmXV0A2aoaiGu\ntqCV91q830m4e7/PLnoOSBKRo3E1DFd4z83B3ZK4XKqaKSJ/Bi4Xkea4Go01AS95ynvdWyLygjd4\nT0/gVBF5wHtNbaBJReswxtQ8ViAw5tCU6kNQjqIxFGKBl1V1BBR/iNfCffgH1siVN15AqWGwRaQp\n8HXAU7FAXpm+DCfiBq3xByzfTyWDPHkjWH6Aq1V4G1f4aFtJtv3eunuo6o/eMo4HvsPVNBjz2xRl\nfQisycCY0PoA6C8iDUTEh2vvvxU3al9HETlBRGJwTQ5lLQH+5I3s1wD4EFcbkQ/UUtU9wGYRuRJA\nRHp58wC8C1zp/X2JN19FOgBbVHUCrmbjfEqPgPd/3vL7AxtVNQd4H69ZQURaAuuAhEPbJMaYmsAK\nBMaEkKquBcbiPkA/x51jD3tNBcNxH9yf4DoWlpUGZANrvdcNV9UsYAEwTUQ64T6sbxCRdcDfcJ0B\n/cDNwKXe832ArEpiLgJiROQLXAfEDKBRwPTmXvPI7cA13nPDcQWadcBLwFVeNmN+u6KsD4GNdmiM\nMcZUQd2z76r+0Q6XP2yjHRpjjDERLcouqK1AYIwxxlRFlN262PoQGGOMMcZqCIwxxpgqibImA6sh\nMMYYY4zVEBhjjDFVYn0IjDHGGBNtrIbAGGOMqQrrQ2CMMcaYaGM1BMYYY0xVWB8CY4wxxkQbqyEw\nxhhjqiLMfQi8kVPTgNbAPuAGVd0SML0fcD9uxNRZqjqzsuVZDYExxhhTM10MxKvq2cBdwPiiCSJS\nG5gAnAd0AwaLyLGVLcwKBMYYY0xVhH/44y644dFR1RVA+4BpLYAtqvqTqu4HPgK6VrYwazIwxhhj\nqiB39eRfbWjiCtQD9gQ8LhCRWqqaX860LOCIyhZmNQTGGGNMzZQJJAc8jvEKA+VNSwZ+rmxhViAw\nxhhjaqZlQB8AEekIrA+Y9iXQTESOEpE6uOaC5ZUtzOePsjstGWOMMb8FAd8yOAPwAdcBKUCSqs4I\n+JZBDO5bBlMqW54VCIwxxhhjTQbGGGOMsQKBMcYYY7ACgTHGGGOwAoExxhhjsAKBMcYYY7ACgTHG\nGGOwAoExxhhjsAKBMcYYY4D/AeQFCnXIYRzdAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1203cb0d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(9,9))\n",
    "confusion_matrix = pd.DataFrame(data = cm_normalized)\n",
    "sns.heatmap(confusion_matrix, annot=True, fmt=\".3f\", linewidths=.5, square = True, cmap = 'Blues_r');\n",
    "plt.ylabel('Actual label');\n",
    "plt.xlabel('Predicted label');\n",
    "all_sample_title = '60000 Training Samples Score: {0}'.format(five_thousand_score) \n",
    "plt.title(all_sample_title, size = 15);\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Display misclassified images with predicted labels"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [conda root]",
   "language": "python",
   "name": "conda-root-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
